Ecological and societal disruptions by modern climate change are critically determined by the time frame over which climates shift beyond historical analogues. Here we present a new index of the year when the projected mean climate of a given location moves to a state continuously outside the bounds of historical variability under alternative greenhouse gas emissions scenarios. Using 1860 to 2005 as the historical period, this index has a global mean of 2069 (±18 years s.d.) for near-surface air temperature under an emissions stabilization scenario and 2047 (±14 years s.d.) under a 'business-as-usual' scenario. Unprecedented climates will occur earliest in the tropics and among low-income countries, highlighting the vulnerability of global biodiversity and the limited governmental capacity to respond to the impacts of climate change. Our findings shed light on the urgency of mitigating greenhouse gas emissions if climates potentially harmful to biodiversity and society are to be prevented.

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.

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.

Spatially continuous data products are essential for a number of applications including climate and hydrologic modeling, weather prediction, and water resource management. In this work, a distance-weighted interpolation method used to map daily rainfall and temperature in Hawaii is described and assessed. New high-resolution (250 m) maps were developed for daily rainfall and daily maximum (Tmax) and minimum (Tmin) near-surface air temperature for the period 1990–2014. Maps were produced using climatologically aided interpolation, in which station anomalies were interpolated using an optimized inverse distance weighting approach and then combined with long-term means to produce daily gridded estimates. Leave-one-out cross validation was performed to assess the quality of the final daily grids. The median absolute prediction error for rainfall was 0.1 mm with an average overprediction (+0.6 mm) on days when total rainfall was less than 1 mm. On days with total rainfall greater than 1 mm, median absolute prediction errors were 2 mm and rainfall was typically underpredicted above the 10-mm threshold. For daily temperature, median absolute prediction errors were 3.1° and 2.8°C for Tmax and Tmin, respectively. On average, this method overpredicted Tmax (+1.1°C) and Tmin (+1.5°C), and errors varied considerably among stations. Errors for all variables exhibited significant seasonal variations. However, the annual range of errors was small. The methods presented here provide an effective approach for mapping daily weather fields in a topographically diverse region and improve on previous products in their spatial resolution, time period of coverage, and use of data.

Long-term, accurate observations of atmospheric phenomena are essential for a myriad of applications, including historic and future climate assessments, resource management, and infrastructure planning. In Hawai‘i, climate data are available from individual researchers, local, State, and Federal agencies, and from large electronic repositories such as the National Centers for Environmental Information (NCEI). Researchers attempting to make use of available data are faced with a series of challenges that include: (1) identifying potential data sources; (2) acquiring data; (3) establishing data quality assurance and quality control (QA/QC) protocols; and (4) implementing robust gap filling techniques. This paper addresses these challenges by providing: (1) a summary of the available climate data in Hawai‘i including a detailed description of the various meteorological observation networks and data accessibility, and (2) a quality controlled meteorological dataset across the Hawaiian Islands for the 25-year period 1990-2014. The dataset draws on observations from 471 climate stations and includes rainfall, maximum and minimum surface air temperature, relative humidity, wind speed, downward shortwave and longwave radiation data.

Growing evidence suggests short-duration climate events may drive community structure and composition more directly than long-term climate means, particularly at ecotones where taxa are close to their physiological limits. Here we use an empirical habitat model to evaluate the role of microclimate during a strong El Niño in structuring a tropical montane cloud forest's upper limit and composition in Hawai'i. We interpolate climate surfaces, derived from a high-density network of climate stations, to permanent vegetation plots. Climatic predictor variables include (1) total rainfall, (2) mean relative humidity, and (3) mean temperature representing non-El Niño periods and a strong El Niño drought. Habitat models explained species composition within the cloud forest with non-El Niño rainfall; however, the ecotone at the cloud forest's upper limit was modeled with relative humidity during a strong El Niño drought and secondarily with non-El Niño rainfall. This forest ecotone may be particularly responsive to strong, short-duration climate variability because taxa here, particularly the isohydric dominant Metrosideros polymorpha, are near their physiological limits. Overall, this study demonstrates moisture's overarching influence on a tropical montane ecosystem, and suggests that short-term climate events affecting moisture status are particularly relevant at tropical ecotones. This study further suggests that predicting the consequences of climate change here, and perhaps in other tropical montane settings, will rely on the skill and certainty around future climate models of regional rainfall, relative humidity, and El Niño.

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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