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Mallory L. Barnes
,
Tomoaki Miura
, and
Thomas W. Giambelluca

Abstract

A comprehensive understanding of the spatial, seasonal, and diurnal patterns in cloud cover frequency over the Hawaiian Islands was developed using high-resolution image data from the National Aeronautics and Space Administration’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellites. The Terra and Aqua MODIS cloud mask products, which provide the confidence that a given 1-km pixel is unobstructed by cloud, were obtained for the entire MODIS time series (10-plus years) over the main Hawaiian Islands. Monthly statistics were generated from the daily cloud mask data, including mean cloud cover frequency at the four daily overpass times. The derived mean cloud cover frequency showed patterns that were generally consistent with the known distribution of mean rainfall and with the results from previous studies. Cloud cover frequency was the highest over land areas with elevations between the lifting condensation level (~600 m) and the mean height of the trade wind inversion (TWI) base (~2200 m), especially for the windward (northeastern) mountain slopes. Above the TWI, cloud frequency decreased sharply with elevation. Irrespective of season, cloud cover frequency was generally higher in the afternoon than in the morning and higher in daytime than at nighttime although these trends varied spatially. The dry season months (May–October) were less cloudy than the wet season months (November–April) at nighttime. The analysis also revealed a local December–January minimum in the annual cycle of cloud cover frequency. The monthly time series produced in this study is the first high-spatial-resolution cloud cover dataset in Hawaii.

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Ryan J. Longman
,
Henry F. Diaz
, and
Thomas W. Giambelluca

Abstract

Consistent increases in the strength and frequency of occurrence of the trade wind inversion (TWI) are identified across a ~40-yr period (1973–2013) in Hawaii. Changepoint analysis indicates that a marked shift occurred in the early 1990s resulting in a 20% increase in the mean TWI frequency between the periods 1973–90 and 1991–2013, based on the average of changes at two sounding stations and two 6-month (dry and wet) seasons. Regional increases in the atmospheric subsidence are identified in four reanalysis datasets over the same ~40-yr time period. The post-1990 period mean for the NCEP–NCAR reanalysis shows increases in subsidence of 33% and 41% for the dry and wet seasons, respectively. Good agreement was found between the time series of TWI frequency of occurrence and omega, suggesting that previously reported increases in the intensity of Hadley cell subsidence are driving the observed increases in TWI frequency. Correlations between omega and large-scale modes of internal climate variability such as El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) do not explain the abrupt shift in TWI frequency in the early 1990s in both seasons. Reported increases in TWI frequency of occurrence may provide some explanation for climate change–related precipitation change at high elevations in Hawaii. On average, post-1990 rainfall was 6% lower in the dry season and 31% lower in the wet season at nine high-elevation sites. Rainfall was significantly correlated with TWI frequency at all of the stations analyzed.

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Guangxia Cao
,
Thomas W. Giambelluca
,
Duane E. Stevens
, and
Thomas A. Schroeder

Abstract

Using 1979–2003 radiosonde data at Hilo and Līhu‘e, Hawaii, the trade wind inversion (TWI) is found to occur approximately 82% of the time at each station, with average base heights of 2225 m (781.9 hPa) for Hilo and 2076 m (798.8 hPa) for Līhu‘e. A diurnal pattern in base height of nighttime high and afternoon low is consistently found during summer at Hilo. Inversion base height has a September maximum and a secondary maximum in April. Frequency of inversion occurrence was found to be higher during winters and lower during summers of El Niño years than non–El Niño years. Significant upward trends were found for inversion frequency at Hilo for March–May (MAM), June–August (JJA), and September–November (SON) seasons, and at Līhu‘e for all seasons and for annual values.

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Henry F. Diaz
,
Eugene R. Wahl
,
Eduardo Zorita
,
Thomas W. Giambelluca
, and
Jon K. Eischeid

Abstract

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December–February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season’s rainfall over the period 1500–2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño–Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance.

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Andrew J. Newman
,
Martyn P. Clark
,
Ryan J. Longman
, and
Thomas W. Giambelluca

Abstract

This study presents a gridded meteorology intercomparison using the State of Hawaii as a testbed. This is motivated by the goal to provide the broad user community with knowledge of interproduct differences and the reasons differences exist. More generally, the challenge of generating station-based gridded meteorological surfaces and the difficulties in attributing interproduct differences to specific methodological decisions are demonstrated. Hawaii is a useful testbed because it is traditionally underserved, yet meteorologically interesting and complex. In addition, several climatological and daily gridded meteorology datasets are now available, which are used extensively by the applications modeling community, thus an intercomparison enhances Hawaiian specific capabilities. We compare PRISM climatology and three daily datasets: new datasets from the University of Hawai‘i and the National Center for Atmospheric Research, and Daymet version 3 for precipitation and temperature variables only. General conclusions that have emerged are 1) differences in input station data significantly influence the product differences, 2) explicit prediction of precipitation occurrence is crucial across multiple metrics, and 3) attribution of differences to specific methodological choices is difficult and limits the usefulness of intercomparisons. Because generating gridded meteorological fields is an elaborate process with many methodological choices interacting in complex ways, future work should 1) develop modular frameworks that allows users to easily examine the breadth of methodological choices, 2) collate available nontraditional high-quality observational datasets for true out-of-sample validation and make them publicly available, and 3) define benchmarks of acceptable performance for methodological components and products.

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Ryan J. Longman
,
Oliver Elison Timm
,
Thomas W. Giambelluca
, and
Lauren Kaiser

Abstract

Undisturbed trade-wind conditions compose the most prevalent synoptic weather pattern in Hawai‘i and produce a distinct pattern of orographic rainfall. Significant total rainfall contributions and extreme events are linked to four types of atmospheric disturbances: cold fronts, kona lows, upper-tropospheric disturbances, and tropical cyclones. In this study, a 20-yr (1990–2010) categorical disturbance time series is compiled and analyzed in relation to daily rainfall over the same period. The primary objective of this research is to determine how disturbances contribute to total wet-season rainfall on the Island of O‘ahu, Hawai‘i. On average, 41% of wet-seasonal rainfall occurs on disturbance days. A total of 17% of seasonal rainfall can be directly attributed to disturbances (after a background signal is removed) and as much as 48% in a single season. The intensity of disturbance rainfall (mm day−1) is a stronger predictor (r 2 = 0.49; p < 0.001) of the total seasonal rainfall than the frequency of occurrence (r 2 = 0.11; p = 0.153). Cold fronts are the most common disturbance type; however, the rainfall associated with fronts that cross the island is significantly higher than rainfall produced from noncrossing fronts. In fact, noncrossing fronts produce significantly less rainfall than under mean nondisturbance conditions 76% of the time. While the combined influence of atmospheric disturbances can account for almost one-half of the rainfall received during the wet season, the primary factor in determining a relatively wet or dry season/year on Oʻahu is the frequency and rainfall intensity of kona low events.

Open access
Andrew J. Newman
,
Martyn P. Clark
,
Ryan J. Longman
,
Eric Gilleland
,
Thomas W. Giambelluca
, and
Jeffrey R. Arnold

Abstract

It is a major challenge to develop gridded precipitation and temperature estimates that adequately resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is particularly pronounced because the available station networks are irregularly spaced and sparse, creating large uncertainties in gridded spatial meteorological estimates. Here a 100-member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for the period 1990–2014 at 1-km grid resolution is developed. First, an intermediary ensemble estimate of the monthly climatological precipitation and temperature is created, and those climatological surfaces are used to inform daily anomaly interpolation. This climatologically aided interpolation (CAI) method extends our initial ensemble system developed for the continental United States. This study demonstrates that direct interpolation of daily precipitation values is inferior to the CAI methodology, particularly over longer time periods (from years to decades). Daily interpolation performs better for short time periods (e.g., 1 month or less) or when the precipitation distribution substantially diverges from climatology. The CAI ensemble is able to reproduce observed precipitation and temperature patterns, including precipitation occurrence. Leave-one-out cross-validation results illustrate that the ensemble has 1) minimal bias for precipitation and temperature; 2) a mean absolute error of 2.5 mm day−1, 1.0 K, and 2.2 K for precipitation and mean and diurnal temperature, respectively; 3) a mean absolute error of 3.3 mm day−1 for the standard deviation of precipitation; and 4) nearly unbiased probability distributions across multiple thresholds of precipitation intensity. Additionally, the ensemble provides estimates of uncertainty across the distributions with increasing uncertainty for higher percentiles.

Open access
Matthew P. Lucas
,
Ryan J. Longman
,
Thomas W. Giambelluca
,
Abby G. Frazier
,
Jared Mclean
,
Sean B. Cleveland
,
Yu-Fen Huang
, and
Jonghyun Lee

Abstract

Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R 2 = 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP; http://www.hawaii.edu/climate-data-portal).

Significance Statement

A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.

Full access
Alison D. Nugent
,
Ryan J. Longman
,
Clay Trauernicht
,
Matthew P. Lucas
,
Henry F. Diaz
, and
Thomas W. Giambelluca

Abstract

Hurricane Lane (2018) was an impactful event for the Hawaiian Islands and provided a textbook example of the compounding hazards that can be produced from a single storm. Over a 4-day period, the island of Hawaiʻi received an island-wide average of 424 mm (17 in.) of rainfall, with a 4-day single-station maximum of 1,444 mm (57 in.), making Hurricane Lane the wettest tropical cyclone ever recorded in Hawaiʻi (based on all available quantitative records). Simultaneously, fires on the islands of nearby Maui and Oʻahu burned 1,043 ha (2,577 ac) and 162 ha (400 ac), respectively. Land-use characteristics and antecedent moisture conditions exacerbated fire hazard, and both fire and rain severity were influenced by the storm environment and local topographical features. Broadscale subsidence around the storm periphery and downslope winds resulted in dry and windy conditions conducive to fire, while in a different region of the same storm, preexisting convection, incredibly moist atmospheric conditions, and upslope flow brought intense, long-duration rainfall. The simultaneous occurrence of rain-driven flooding and landslides, high-intensity winds, and multiple fires complicated emergency response. The compounding nature of the hazards produced during the Hurricane Lane event highlights the need to improve anticipation of complex feedback mechanisms among climate- and weather-related phenomena.

Free access
Thomas W. Giambelluca
,
Dirk Hölscher
,
Therezinha X. Bastos
,
Reginaldo R. Frazão
,
Michael A. Nullet
, and
Alan D. Ziegler

Abstract

Regional climatic change, including significant reductions in Amazon Basin evaporation and precipitation, has been predicted by numerical simulations of total tropical forest removal. These results have been shown to be very sensitive to the prescription of the albedo shift associated with conversion from forest to a replacement land cover. Modelers have so far chosen to use an “impoverished grassland” scenario to represent the postforest land surface. This choice maximizes the shifts in land surface parameters, especially albedo (fraction of incident shortwave radiation reflected by the surface). Recent surveys show secondary vegetation to be the dominant land cover for some deforested areas of the Amazon. The characteristics of secondary vegetation as well as agricultural land covers other than pasture have received little attention from field scientists in the region. This paper presents the results of field measurements of radiation flux over various deforested surfaces on a small farm in the eastern Amazonian state of Pará. The albedo of fields in active use was as high as 0.176, slightly less than the 0.180 recently determined for Amazonian pasture and substantially less than the 0.19 commonly used in GCM simulations of deforestation. For 10-yr-old secondary vegetation, albedo was 0.135, practically indistinguishable from the recently published mean primary forest albedo of 0.134. Measurements of surface temperature and net radiation show that, despite similarity in albedo, secondary vegetation differs from primary forest in energy and mass exchange. The elevation of midday surface temperature above air temperature was found to be greatest for actively and recently farmed land, declining with time since abandonment. Net radiation was correspondingly lower for fields in active or recent use. Using land cover analyses of the region surrounding the study area for 1984, 1988, and 1991, the pace of change in regional-mean albedo is estimated to have declined and appears to be leveling at a value less than 0.03 above that of the original forest cover.

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