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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State of Hawai'i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near-real time.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 [...]

Drought is a prominent feature of Hawaiʻi's climate. However, it has been over 30 years since the last comprehensive meteorological drought analysis, and recent drying trends have emphasized the need to better understand drought dynamics and multi-sector effects in Hawaiʻi. Here, we provide a comprehensive synthesis of past drought effects in Hawaiʻi that we integrate with geospatial analysis of drought characteristics using a newly developed 100-year (1920–2019) gridded Standardized Precipitation Index (SPI) dataset. The synthesis examines past droughts classified into five categories: Meteorological, agricultural, hydrological, ecological, and socioeconomic drought. Results show that drought duration and magnitude have increased significantly, consistent with trends found in other Pacific Islands. We found that most droughts were associated with El Niño events, and the two worst droughts of the past century were multi-year events occurring in 1998–2002 and 2007–2014. The former event was most severe on the islands of O'ahu and Kaua'i while the latter event was most severe on Hawaiʻi Island. Within islands, we found different spatial patterns depending on leeward versus windward contrasts. Droughts have resulted in over $80 million in agricultural relief since 1996 and have increased wildfire risk, especially during El Niño years. In addition to providing the historical context needed to better understand future drought projections and to develop effective policies and management strategies to protect natural, cultural, hydrological, and agricultural resources, this work provides a framework for conducting drought analyses in other tropical island systems, especially those with a complex topography and strong climatic gradients. [ABSTRACT FROM AUTHOR]

Drought is a growing threat to hydrological, ecological, agricultural, and socio-cultural systems of the tropics, especially tropical islands of the Pacific where severe droughts can compromise food and water security. Overcoming barriers to knowledge sharing between land managers and researchers is a critical cross-sector strategy for engaging and mitigating or adapting to drought. Here we describe the establishment and functioning of the Pacific Drought Knowledge Exchange (PDKE), which provides users with easier access to: (1) sector- and geography-specific climate information; (2) better and more comprehensive information; (3) improved technical assistance; and (4) a more collaborative information-transfer environment through participation in knowledge co-production. We focus on our collaborative work with managers of important tropical dryland ecosystems from three distinct geographies to pilot the collaborative development of climate change, climate variability, and drought "portfolios" featuring site-specific historical and forecasted future information. This information was then used to collaboratively produce factsheets that partners used to: (i) better understand past and projected climate for specific management units; (ii) integrate new climate knowledge into management planning; and (iii) support climate-focused educational and outreach efforts. This pilot effort demonstrates the successful application of climate-focused co-production in dry tropical landscapes. [ABSTRACT FROM AUTHOR]

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), highresolution (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 (R² = 0.78; MAE = 55 mm month21; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month21; 21%) 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). [ABSTRACT FROM AUTHOR]

Climate Change Response Framework The Northern Institute of Applied Climate Science (NIACS; www.niacs.org) has been collaboratively working with land managers since 2009 to conduct climate change vulnerability assessments and develop adaptation strategies and approaches though a Climate Change Response Framework (CCRF; https://forestadaptation.org/; [17]). Participants in the Hawai'i APP Training brought a broad diversity of knowledge regarding potential future impacts of climate change and desired management goals and objectives. Keywords: Ecology; Tropics; Climate variability; Adaptation EN Ecology Tropics Climate variability Adaptation E402 E409 8 03/09/22 20220201 NES 220201 Adaptation Planning and Practices for Hawai'i Forests and Native EcosystemsWhat: More than 40 participants, representing federal and state government agencies, nongovernmental organizations, academia, and private landholders met remotely to receive practical training in considering climate change information and identifying adaptation actions for natural resources management professionals working in forests and native Hawaiian ecosystems. [Extracted from the article]

Summer precipitation in Hawai'i accounts for 40% of the annual total and provides important water sources. However, our knowledge about its variability remains limited. Here we show that statewide Hawai'i summer rainfall (HSR) variability exhibits two distinct regimes: quasi‐biennial (QB, ~2 years) and interdecadal (~30–40 years). The QB variation is linked to alternating occurrences of the Western North Pacific (WNP) cyclone and anticyclone in successive years, which is modulated by the intrinsic El Niño–Southern Oscillation biennial variability and involves a positive feedback between atmospheric Rossby waves and underlying sea surface temperature (SST) anomalies. The interdecadal variation of HSR is largely modulated by the Pacific Decadal Oscillation through affecting upstream low‐level humidity that affects topographic rainfall. HSR shows weak long‐term drying trend during 1920–2019. This first description of the major physical drivers of summer rainfall variability provides key information for seasonal rainfall prediction in Hawai'i. Plain Language Summary: Summer, dry season, precipitation in Hawai'i accounts for 40% of its annual total and provides an important water source for the State of Hawai'i. However, there is a lack of knowledge about how and why summer rainfall varies. Here, we explore the variability in Hawaiian summer rainfall and the associated mechanisms that drive rainfall variations. We show that the statewide Hawai'i summer rainfall index has two notable periods: quasi‐biennial (QB, ~2 years) and interdecadal (~30–40 years). QB rainfall variability is linked the El Niño–Southern Oscillation biennial variability. The interdecadal variability is linked to sea surface temperature variations associated with the Pacific Decadal Oscillation. The long‐term trend in summer rainfall shows a weak drying during 1920–2019. This study systematically identifies the major factors driving summer rainfall variability for the first time. Our results provide clues to help predict Hawai'i summer rainfall. Key Points: Summer rainfall in Hawai'i has two distinct regimes: quasi‐biennial and interdecadal, but no significant trend during 1920–2019The quasi‐biennial variability roots in biennial variation of the western North Pacific High driven by ENSO and atmosphere‐ocean interactionThe interdecadal variation of summer rainfall in Hawai'i is modulated by PDO through affecting the upstream low‐level humidity [ABSTRACT FROM AUTHOR]

Climate change is expected to alter precipitation patterns worldwide, which will affect streamflow in riverine ecosystems. It is vital to understand the impacts of projected flow variations, especially in tropical regions where the effects of climate change are expected to be one of the earliest to emerge. Space‐for‐time substitutions have been successful at predicting effects of climate change in terrestrial systems by using a spatial gradient to mimic the projected temporal change. However, concerns have been raised that the spatial variability in these models might not reflect the temporal variability. We utilized a well‐constrained rainfall gradient on Hawaii Island to determine (a) how predicted decreases in flow and increases in flow variability affect stream food resources and consumers and (b) if using a high temporal (monthly, four streams) or a high spatial (annual, eight streams) resolution sampling scheme would alter the results of a space‐for‐time substitution. Declines in benthic and suspended resource quantity (10‐ to 40‐fold) and quality (shift from macrophyte to leaf litter dominated) contributed to 35‐fold decreases in macroinvertebrate biomass with predicted changes in the magnitude and variability in the flow. Invertebrate composition switched from caddisflies and damselflies to taxa with faster turnover rates (mosquitoes, copepods). Changes in resource and consumer composition patterns were stronger with high temporal resolution sampling. However, trends and ranges of results did not differ between the two sampling regimes, indicating that a suitable, well‐constrained spatial gradient is an appropriate tool for examining temporal change. Our study is the first to investigate resource to community wide effects of climate change on tropical streams on a spatial and temporal scale. We determined that predicted flow alterations would decrease stream resource and consumer quantity and quality, which can alter stream function, as well as biomass and habitat for freshwater, marine, and terrestrial consumers dependent on these resources. A space‐for‐time‐substitution along a rainfall gradient, which mimics climate change effects, predicts declines in resource and consumer biomass and shifts in benthic composition for tropical streams. Our two different sampling regimes (high temporal resolution (HTR) vs. high spatial resolution (HSR)) produced similar predictions. [ABSTRACT FROM AUTHOR]

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]

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.