Downscaling of climate model output for Alaskan stakeholders

Walsh, John E.; Bhatt, Uma S.; Littell, Jeremy S.; Leonawicz, Matthew; Lindgren, M.; Kurkowski, Thomas A.; Bieniek, Peter A.; Thoman, Richard; Gray, Stephen T.; Rupp, T. Scott

doi:10.1016/j.envsoft.2018.03.021

Cited by 60 publications

(55 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We obtained historical and CMIP5 bias corrected delta method downscaled temperature and precipitation projections from SNAP for five GCMs: NCAR CCSM4, NOAA GFDL-CM3, NASA GISS-E2-R, IPSL-CM5ALR, and MRI-CGCM3. Following Walsh et al [37], Walsh et al [38] showed these models to perform well over the historical period in the Arctic and Alaska and to represent a range of plausible climate scenarios for the region. We chose to use downscaled projections (771 m decadally averaged monthly values) for RCP 4.5 and RCP 8.5 emissions scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…The analyses presented here rely primarily on the five-model mean response for five climate models. Walsh et al [38] show that the inclusion of a greater number of models would not likely alter mean projected responses, and the five-models serve well to bracket the range of future conditions under the CMIP3 or CMIP5 scenarios and climate models. While the mean scenario may be useful for projecting a range of likely plausible conditions, the dynamics of individual GCMs are not compared here beyond the PSF analysis (Figure 3).…”

Section: Projected Changes In Psf Sfe and Sfe:pmentioning

confidence: 99%

See 1 more Smart Citation

Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios

Littell

McAfee

Hayward

2018

Water

View full text Add to dashboard Cite

Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October-March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040-2069) and late-century (2070-2099) changes in PSF, SFE, and SFE:P relative to historical (1970-1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040-2069 and the largest for RCP 8.5 in 2070-2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Projected Changes In Psf Sfe and Sfe:pmentioning

confidence: 99%

Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios

Littell

McAfee

Hayward

2018

Water

View full text Add to dashboard Cite

show abstract

“…Statistical downscaling is advantageous in that it requires a small amount of calculation, simple to use and does not consider the effects of boundary conditions on the predicted results [23]. A statistical downscaling method called the Delta method, which is simple and easy to operate, has been widely used for projection of future climate change around the world [22,[24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

et al. 2019

View full text Add to dashboard Cite

In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based method was used to rank the performance of the GCMs over the LMB. Two methods of multi-model ensemble (MME), sub-ensemble from the top 25% ranked GCMs and full ensemble from the entire GCMs, were calculated using arithmetic mean (AM) method and downscaled using the Delta method to project future temperature change during two future time periods, the near future (2006–2049) and the far future (2050–2093), under representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5 scenarios) over the LMB. The improved score-based method combining multiple criteria showed a robust assessment of the GCMs performance over the LMB, which can provide good information for projecting future temperature change. The results showed a significant increase in temperature over the LMB under the two ensembles. However, there were differences in the magnitudes of the future temperature increase between the two ensemble methods, with a higher mean annual temperature increase from full ensemble and sub-ensemble at 1.26 °C (1.09 °C), 1.90 °C (1.70 °C), and 2.97 °C (2.78 °C) during 2050–2093 under the RCP2.6, RCP4.5, and RCP8.5 scenarios compared to the values at 0.93 °C (0.87 °C), 0.99 °C (0.95 °C), and 1.09 °C (1.06 °C) during 2006–2049, respectively, relative to the reference time period of 1961–2004. In the future (2006–2093), the temperature is likely to increase at 0.04 °C, 0.16 °C, and 0.37 °C decade-1 under the RCP2.6, RCP4.5, and RCP8.5 scenarios by the sub-ensemble, while a higher temperature increase at 0.05 °C, 0.17 °C, and 0.39 °C was found by the full ensemble over the LMB, relative to the reference time period of 1961–2004. On the whole, the higher warming mainly occurred in the northern and central areas over the LMB, while the lower warming mainly occurred in the southeast and the southwest, especially under the RCP4.5 and RCP8.5 scenarios, with the warming increased with increasing RCP for both ensembles. Moreover, in order to reduce the uncertainty of temperature projection in further studies in the LMB, multiple methods of GCMs ensemble should be considered and compared.

show abstract

“…By applying the same adjustment to the models' future output, we are making the assumption that the models' biases do not change systematically in the future. Similar assumptions are made in bias-corrections of model projections of other variables, e.g., temperature and precipitation, when the Delta-method is applied in downscaling applications [24] [25] [26]. While there is no proof that this assumption is valid, we are not aware of any evidence to the contrary.…”

Section: Datasets and Methodologymentioning

confidence: 93%

Climatological Characteristics of Historical and Future High-Wind Events in Alaska

Basu¹,

Walsh²

2018

ACS

Self Cite

View full text Add to dashboard Cite

High winds cause waves, storm surge, erosion and physical damage to infrastructure and ecosystems. However, there have been few evaluations of wind climatologies and future changes, especially change in high-wind events, on a regional basis. This study uses Alaska as a regional case study of climatological wind speed and direction. Eleven first-order stations across different subregions of Alaska provide historical data (1975-2005) for the observational climatology and for the calibration of Coupled Model Inter comparison Project (CMIP5) simulations, which in turn provide projections of changes in winds through 2100. Historically, winds exceeding 25 and 35 knots are most common in the Bering Sea coastal region of Alaska, followed by northern Alaska coastal areas. Autumn and winter are the seasons of most frequent high-wind occurrences in the coastal sites, while there is no distinct seasonal peak at the interior stations where high-wind events are less frequent. An examination of the sea level pressure pattern associated with the highest-wind event at each station reveals the presence of a strong pressure gradient associated with an extratropical cyclone in most cases. Northern coastal regions of Alaska are projected to experience increased frequencies of high-wind events during the cold season, especially late autumn and early winter, when reduced sea ice cover in the late century will leave coastal regions increasingly vulnerable to flooding and erosion.

show abstract

Downscaling of climate model output for Alaskan stakeholders

Cited by 60 publications

References 21 publications

Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios

Alaska Snowpack Response to Climate Change: Statewide Snowfall Equivalent and Snowpack Water Scenarios

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

Climatological Characteristics of Historical and Future High-Wind Events in Alaska

Contact Info

Product

Resources

About