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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.

The ongoing emission of greenhouse gases is triggering changes in many climate hazards that can impact humanity. We found traceable evidence for 467 pathways in which human health, water, food, economy, infrastructure, and security have been recently impacted by climate hazards such as warming, heatwaves, precipitation, drought, floods, fires, storms, sea level rise, and changes in natural land cover and ocean chemistry. By 2100, the world's population will be exposed concurrently to the equivalent of the largest magnitude in one of these hazards if greenhouse gasses are aggressively reduced or three if they are not, with some tropical coastal areas facing up to six hazards concurrently. These findings highlight that greenhouse gas emissions pose a broad threat to humanity by simultaneously intensifying many hazards that have been harmful to numerous aspects of human life.Ongoing greenhouse gas emissions are simultaneously shifting many elements of Earth's climate beyond thresholds that can impact humanity 1 . By affecting the balance between incoming solar radiation and outgoing infrared radiation, man-made greenhouse gases are increasing the Earth's energy budget ultimately leading to warming 1 . Given interconnected physics, warming can affect other aspects of the Earth's climate system 2 . For instance, by enhancing water evaporation and increasing the air's capacity to hold moisture, warming can lead to drought in commonly dry places, in turn ripening conditions for wildfires and heatwaves when heat transfer from water evaporation ceases. There are opposite responses in commonly humid places where constant evaporation leads to more precipitation, which is commonly followed by floods due to soil saturation. The oceans have the added effect of sea warming, which enhances evaporation and wind speeds, intensifying downpours and the strength of storms, whose surges can be aggravated by sea level rise resulting from the larger volume occupied by warmed water molecules and melting land ice. Other inter-related changes in the ocean include acidification as CO2 mixes with water to form carbonic acid, and reduced oxygen due to warming reducing oxygen solubility and affecting circulation patterns and the mixing of surface waters rich in oxygen with deeper oxygen-poor water. These climate hazards and their impacts on human societies occur naturally but are being nontrivially intensified by man-made greenhouse gas emissions, as demonstrated by an active research on detection and attribution (discussed under Caveats in the Methods section). With few exceptions 3 , changes in these hazards have been studied in isolation whereas impact assessments have commonly focused on specific aspects of human life. Unfortunately, the failure to integrate available information most likely underestimates the impacts of climate change because i) one hazard may be important in one place but not another, ii) strong CO2 reductions may curb some but not all hazards (See Fig. S1), and iii) not all aspects of human systems are equally challenge...

ABSTRACT:The Hawaiian Islands have one of the most spatially diverse rainfall patterns on earth. Knowledge of these patterns is critical for a variety of resource management issues and, until now, only long-term mean monthly and annual rainfall maps have been available for Hawai'i. In this study, month-year rainfall maps from January 1920 to December 2012 were developed for the major Hawaiian Islands. The maps were produced using climatologically aided interpolation (CAI), where the station anomalies were interpolated first, and then combined with the mean maps. A geostatistical method comparison was performed to choose the best interpolation method. The comparison focuses on three kriging algorithms: ordinary kriging (OK), ordinary cokriging (OCK), and kriging with an external drift (KED). Two covariates, elevation and mean rainfall, were tested with OCK and KED. The combinations of methods and covariates were compared using cross-validation statistics, where OK produced the lowest error statistics. Station anomalies for each month were interpolated using OK and combined with the mean monthly maps to produce the final month-year rainfall maps.

Spatial patterns of rainfall in Hawai‘i are among the most diverse in the world. As the global climate warms, it is important to understand observed rainfall variations to provide context for future changes. This is especially important for isolated oceanic islands where freshwater resources are limited, and understanding the potential impacts of climate change on the supply of freshwater is critical. Utilizing a high‐resolution gridded data set of monthly and annual rainfall for Hawai‘i from January 1920 to December 2012, seasonal and annual trends were calculated for every 250‐m pixel across the state and mapped to produce spatially continuous trend maps. To assess the stability of these trends, a running trend analysis was performed on 34 selected stations. From 1920 to 2012, over 90% of the state experienced drying trends, with Hawai‘i Island, and in particular the western part of the island, experiencing the largest significant long‐term declines in annual and dry season rainfall. The running trend analysis highlighted the multi‐decadal variability present in these trends, and revealed that the only region in the state with persistent annual and dry season trends through the study period is the western part of Hawai‘i Island; for most other regions, the drying trends were not significant until the most recent part of the record was included. These results support previous studies that indicate drying across the state over recent decades, and reveal the timing of upward and downward trends as well as important spatial details for natural resource management in Hawai‘i.

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.