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The Hawai'i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai'i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near-real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The 'virtual station' feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community. SIGNIFICANCE STATEMENT: The Hawai'i Climate Data Portal (HCDP) is an open-source data platform designed to provide easy access to climate data and information for the State of Hawai'i. The HCDP contains both station data and historical and future maps of rainfall and temperature. These high-resolution (250 m) maps are updated in near-real time and available for streamlined visualization and download. The HCDP fills a critical need for a centralized clearinghouse of climate data in the State of Hawai'i. HCDP data can be used to make resource management decisions and to raise awareness of observed and projected changes to the climate system. Many ongoing projects are leveraging HCDP data to develop models and decision support tools to improve adaptive capacities across the state. KEYWORDS: Rainfall; Climate; Temperature; Downscaling; Climate services, 1. Data portal purpose and design The effects of climate change, including higher temperatures (McKenzie et al. 2019), increased drought magnitude and duration (Frazier et al. 2022), decreases in rainfall [...]

Under climate change, ecosystems are experiencing novel drought regimes, often in combination with stressors that reduce resilience and amplify drought's impacts. Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e. ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. Providing a holistic picture of how drought initiates long-term change supports the development of risk assessments, predictive models, and management strategies, enhancing preparedness for a complex and growing challenge. [ABSTRACT FROM AUTHOR]

Aim: To provide the first regional analysis of contemporary drivers of Pacific Island fire regimes. Location: Islands of Palau, Yap, Guam, Rota, Tinian, Saipan, Chuuk, Pohnpei, Kosrae. Time Period: 1950‐present. Methods: We used land cover, soil maps and contemporary fire histories to (1) describe the relationships among fire activity, vegetation, rainfall and island geography and population; (2) examine the spatial associations of forest and savanna vegetation with respect to fire and soil types; and (3) link fire and savanna distribution to intra‐annual and inter‐annual rainfall variability. Results: Savanna extent was positively correlated with island age and the range of mean monthly rainfall. The percent of area burned annually reached upwards of 2%–10% of island land areas, correlated positively with rainfall seasonality, and occurred largely within savannas. Savannas were more frequent on acidic soils with higher aluminium than forests but distributed across all soil types. El Niño intensity correlated negatively with dry season rainfall over the entire study region and positively with annual area burned on Guam. Main Conclusions: The contemporary extents of savannas in Micronesia reflect Pacific Islanders' long‐term use of fire to shape vegetation and are modulated by inter‐ and intra‐annual rainfall variability. These relationships provide baseline information for ongoing fire management and mitigation projects throughout Micronesia and critical insight for assessing and anticipating fire risk in other insular areas where fire data are limited. [ABSTRACT FROM AUTHOR]

While there has been rapid advancement in the development and application of statistical downscaling methods for climate projection, determining the best predictive large‐scale climate information for the targeted local climate variable remains a challenge. The choice of predictor variables is one of the most influential steps of model development and has the potential to lead to varying results, contributing to the total uncertainty of future projections. Despite being a well‐known problem, predictor selection often does not receive adequate attention and the development of a straightforward and feasible prescreening process is needed to provide guidance to simple (e.g., linear regression) and complex (e.g., machine learning) modeling tools alike. In this project, Akaike information criterion (AIC) and leave one out cross validation are used to evaluate sets of predictor variables common in statistical downscaling models of precipitation (e.g., temperature, geopotential height, moisture transport). Because thousands of predictor sets were found to be competitive in their statistical skill in projecting rainfall, results suggest that an ensemble of predictor sets should be used to account for the resulting variance associated with predictor selection. This ensemble approach is applied to make improvements to the most recent statistical downscaling model developed for rainfall projection in Hawai'i. An ensemble validation study is performed by projecting future wet season rainfall in Hawai'i for scenario RCP4.5 using 17 CMIP5 GCMs with ensembles of highly ranked and poorly ranked predictor sets. Results show significantly less variance and improved agreement in the projected sign of precipitation change for the highly ranked predictor set ensemble. In conclusion, it is recommended that statistical downscaling models are run in an ensemble‐mode with multiple combinations of predictors instead of using a single model, allowing for additional quantification of model uncertainty and a "safety net" for when the true physically best‐suited predictor set is unknown. [ABSTRACT FROM AUTHOR]

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., Climate is a primary driver of biological processes, operating from individuals to ecosystems, and affects several aspects of human life. Therefore, climates without modern precedents could cause large and potentially [...]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]