Abstract This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation.
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The Relative Warming Rates of Heat Events and Median Days in the Pacific Northwest from Observations and a Regional Climate Model
Abstract This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-year intervals (85, 90, 95, and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50 th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95 th percentile daily maximum temperature shows a warming rate of up to 0.2°C per decade above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land-atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of Weather Research and Forecasting (WRF) model simulations forced by 12 global climate model simulations from the 5 th Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing.
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- NSF-PAR ID:
- 10396879
- Date Published:
- Journal Name:
- Journal of Climate
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- 1 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Heat waves are increasing in frequency, duration, and intensity and are strongly linked to anthropogenic climate change. However, few studies have examined heat waves in Florida, despite an older population and increasingly urbanized land areas that make it particularly susceptible to heat impacts. Heavy precipitation events are also becoming more frequent and intense; recent climate model simulations showed that heavy precipitation in the three days after a Florida heat wave follow these trends, yet the underlying dynamic and thermodynamic mechanisms have not been investigated. In this study, a heat wave climatology and trend analysis are developed from 1950 to 2016 for seven major airports in Florida. Heat waves are defined based on the 95th percentile of daily maximum, minimum, and mean temperatures. Results show that heat waves exhibit statistically significant increases in frequency and duration at most stations, especially for mean and minimum temperature events. Frequency and duration increases are most prominent at Tallahassee, Tampa, Miami, and Key West. Heat waves in northern Florida are characterized by large-scale continental ridging, while heat waves in central and southern Florida are associated with a combination of a continental ridge and a westward extension of the Bermuda–Azores high. Heavy precipitation events that follow a heat wave are characterized by anomalously large ascent and moisture, as well as strong instability. Light precipitation events in northern Florida are characterized by advection of drier air from the continent, while over central and southern Florida, prolonged subsidence is the most important difference between heavy and light events.
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Abstract Much of our current risk assessment, especially for extreme events and natural disasters, comes from the assumption that the likelihood of future extreme events can be predicted based on the past. However, as global temperatures rise, established climate ranges may no longer be applicable, as historic records for extremes such as heat waves and floods may no longer accurately predict the changing future climate. To assess extremes (present‐day and future) over the contiguous United States, we used NOAA's Climate Extremes Index (CEI), which evaluates extremes in maximum and minimum temperature, extreme one‐day precipitation, days without precipitation, and the Palmer Drought Severity Index (PDSI). The CEI is a spatially sensitive index that uses percentile‐based thresholds rather than absolute values to determine climate “extremeness” and is thus well‐suited to compare extreme climate across regions. We used regional climate model data from the North American Regional Climate Change Assessment Program (NARCCAP) to compare a late 20th century reference period to a mid‐21st century “business as usual” (SRES A2) greenhouse gas‐forcing scenario. Results show a universal increase in extreme hot temperatures across all models, with annual average maximum and minimum temperatures exceeding 90th percentile thresholds consistently across the continental United States. Results for precipitation indicators have greater spatial variability from model to model, but indicate an overall movement towards less frequent but more extreme precipitation days in the future. Due to this difference in response between temperature and precipitation, the mid‐21st century CEI is primarily an index of temperature extremes, with 90th percentile temperatures contributing disproportionately to the overall increase in climate extremeness. We also examine the efficacy of the PDSI in this context in comparison to other drought indices.
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Abstract Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (>60°N) significant upward SAT trend of +0.62°C decade−1since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm.
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Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JCLI-D-22-0313.1
