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Aurora K Kagawa-Viviani

University of Hawaii at Manoa

Subject Areas: hydrology, ecohydrology

 Recent Activity

ABSTRACT:

This collection is associated with the accepted Journal of Geophysical Research- Atmospheres manuscript of the same title.

While the Hawaiian Islands are experiencing long-term warming, spatial and temporal patterns are poorly characterized. Drawing on daily temperature records from 309 stations (1905-2017), we explored relationships of surface air temperatures (Tmax, Tmin, Tavg and DTR) to atmospheric, oceanic, and land surface variables. Statistical modeling of spatial patterns (2006-2017) highlighted the strong negative influence of elevation and moisture on air temperature, and the effects of distance inland, cloud frequency, wind speed, and the local trade wind inversion on the elevation dependence of surface air temperature. We developed time series of sea level air temperature and surface lapse rate by modeling surface air temperature as a simple function of elevation and found a strong long term (1905-2017) warming trend in sea level Tmin, twice that of Tmax (+0.17 vs +0.07 °C decade-1), suggesting regional warming, possibly enhanced by urbanization and cloud cover effects. Removing this trend, sea level Tmax and Tmin tracked SST and rainfall at decadal time scales, while Tmax increased with periods of weakened trade winds. Sea level air temperatures correlated with North Pacific climate indices, reflecting the influence of regional circulation via SST, rain, clouds, and trade winds that modulate environmental warming across the Hawaiian Islands. Increasing (steeper) Tmax surface lapse rates for the 0-1600 m elevation range (into the cloud zone) over 1978-2017 coincide with observations of marine boundary layer drying and rising cloud base heights, suggesting a need to better understand elevation-dependent warming in this tropical/subtropical maritime environment and associated changes to cloud formation and persistence.

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ABSTRACT:

Regression coefficients fitted for the AICc-selected best models of mean daily Tmax, Tmin, Tavg, and DTR for the 12 mean months and for all 144 months from January 2006-2017, fitted with station monthly data from the 2006-2017 period.

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ABSTRACT:

Station IDs, locations, and period of record for 309 surface air temperature stations used for this study.

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ABSTRACT:

Full set of all regression models and fitted coefficients evaluated for mean daily Tmax, Tmin, Tavg, and DTR. Models are ranked by decreasing AICc and were fit using station mean annual values from the 2006-2017 period. Latitude and longitude were excluded from the final model set as they were inconsistent predictors of Tmax, Tmin, Tavg, and DTR, and, when included, obscured interpretation of the fitted model coefficients. Highlighted rows indicate models corresponding to best multiple regression model form after accounting for multicollinearity based on a variance inflation factor < 5. Other highlighted model forms include segmented linear regression of temperature as a function of elevation with a 2150 m breakpoint (Model #129) and simple linear regression of temperature as a function of elevation (Model #1).

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ABSTRACT:

This composite resource contains geotiff files for Tmax, Tmin, and Tavg across the Hawaiian Islands, 250 m spatial resolution, for each month from January 2006 to December 2017. Tmax and Tmin were predicted from regression models of station-month Tmax, Tmin values fitted as a functions of elevation and environmental covariates. Tavg maps were calculated as the arithmetic mean of the corresponding Tmax and Tmin maps. Each geotiff file consists of a stack of 12 raster layers for the diurnal temperature metric and year indicated by the filename (e.g., Tmax_2017stack.tif =maximum daily temperature for each month of 2017).

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Composite Resource Composite Resource

ABSTRACT:

This composite resource contains geotiff files for mean daily Tmax, Tmin, and Tavg across the Hawaiian Islands at 250 m spatial resolution. Tmax and Tmin were modeled by fitting regressions for station average values over the 2006-2017 base period. Tavg was calculated as the arithmetic mean of the Tmax and Tmin maps.

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Composite Resource Composite Resource

ABSTRACT:

This composite resource contains geotiff files for mean daily Tmax, Tmin, and Tavg across the Hawaiian Islands at 250 m spatial resolution. Tmax and Tmin were predicted for January-December based on regression models of station-month Tmax and Tmin values (averaged over 2006-2017) fitted as functions of elevation and environmental covariates. Tavg was calculated as the arithmetic mean of the Tmax and Tmin maps. Each geotiff file consists of a stack of 12 raster layers for months 1-12 (Jan-Dec).

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Composite Resource Composite Resource

ABSTRACT:

This composite resource contains geotiff files for Tmax, Tmin, and Tavg across the Hawaiian Islands, 250 m spatial resolution, for each month from January 2006 to December 2017. Tmax and Tmin were predicted from regression models of station-month Tmax, Tmin values fitted as a functions of elevation and environmental covariates. Tavg maps were calculated as the arithmetic mean of the corresponding Tmax and Tmin maps. Each geotiff file consists of a stack of 12 raster layers for the diurnal temperature metric and year indicated by the filename (e.g., Tmax_2017stack.tif =maximum daily temperature for each month of 2017).

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Composite Resource Composite Resource

ABSTRACT:

Full set of all regression models and fitted coefficients evaluated for mean daily Tmax, Tmin, Tavg, and DTR. Models are ranked by decreasing AICc and were fit using station mean annual values from the 2006-2017 period. Latitude and longitude were excluded from the final model set as they were inconsistent predictors of Tmax, Tmin, Tavg, and DTR, and, when included, obscured interpretation of the fitted model coefficients. Highlighted rows indicate models corresponding to best multiple regression model form after accounting for multicollinearity based on a variance inflation factor < 5. Other highlighted model forms include segmented linear regression of temperature as a function of elevation with a 2150 m breakpoint (Model #129) and simple linear regression of temperature as a function of elevation (Model #1).

Show More
Composite Resource Composite Resource

ABSTRACT:

Station IDs, locations, and period of record for 309 surface air temperature stations used for this study.

Show More
Composite Resource Composite Resource

ABSTRACT:

Regression coefficients fitted for the AICc-selected best models of mean daily Tmax, Tmin, Tavg, and DTR for the 12 mean months and for all 144 months from January 2006-2017, fitted with station monthly data from the 2006-2017 period.

Show More
Collection Resource Collection Resource

ABSTRACT:

This collection is associated with the accepted Journal of Geophysical Research- Atmospheres manuscript of the same title.

While the Hawaiian Islands are experiencing long-term warming, spatial and temporal patterns are poorly characterized. Drawing on daily temperature records from 309 stations (1905-2017), we explored relationships of surface air temperatures (Tmax, Tmin, Tavg and DTR) to atmospheric, oceanic, and land surface variables. Statistical modeling of spatial patterns (2006-2017) highlighted the strong negative influence of elevation and moisture on air temperature, and the effects of distance inland, cloud frequency, wind speed, and the local trade wind inversion on the elevation dependence of surface air temperature. We developed time series of sea level air temperature and surface lapse rate by modeling surface air temperature as a simple function of elevation and found a strong long term (1905-2017) warming trend in sea level Tmin, twice that of Tmax (+0.17 vs +0.07 °C decade-1), suggesting regional warming, possibly enhanced by urbanization and cloud cover effects. Removing this trend, sea level Tmax and Tmin tracked SST and rainfall at decadal time scales, while Tmax increased with periods of weakened trade winds. Sea level air temperatures correlated with North Pacific climate indices, reflecting the influence of regional circulation via SST, rain, clouds, and trade winds that modulate environmental warming across the Hawaiian Islands. Increasing (steeper) Tmax surface lapse rates for the 0-1600 m elevation range (into the cloud zone) over 1978-2017 coincide with observations of marine boundary layer drying and rising cloud base heights, suggesting a need to better understand elevation-dependent warming in this tropical/subtropical maritime environment and associated changes to cloud formation and persistence.

Show More