Search Results

You are looking at 1 - 10 of 12 items for

  • Author or Editor: Lisa Sloan x
  • Refine by Access: All Content x
Clear All Modify Search
Noah S. Diffenbaugh
and
Lisa C. Sloan

Abstract

Within the context of anthropogenic climate change, paleoclimate modeling has become a key technique for studying climate system responses to changes in external forcing. Of current interest is the response of regional-scale climate to global-scale changes in climate forcing, a problem made particularly difficult in regions of topographic complexity. In an effort to understand the role that regional-scale climate processes play in shaping the response of regional climate to changes in external forcing, the sensitivity of a high-resolution regional climate model (RCM) to mid-Holocene orbital forcing was tested, focusing on the Pacific coast region of the western United States as a case study. Mid-Holocene orbital forcing resulted in RCM-simulated summer warming of 1°–2.5°C over most of the western United States. This result is in strong agreement with proxy reconstructions, suggesting that regional mid-Holocene temperature change can be explained by direct orbital forcing alone, independent of climate system feedbacks. In contrast, positive anomalies (mid-Holocene—control) in mean annual precipitation − evaporation (PE), dominated by changes in atmospheric circulation in the seasonal transition months of March and November, were in disagreement with proxy reconstructions from the Pacific coast. This model–data mismatch in moisture characteristics suggests that direct orbital forcing of regional-scale atmospheric processes was not the sole influence shaping the mid-Holocene moisture record of the Pacific coast. It also indicates that consideration of regional-scale climate system feedbacks and extraregional process interactions is critical for the application of RCMs to both paleoclimate problems and future climate change scenarios.

Full access
Jason L. Bell
and
Lisa C. Sloan

Abstract

Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States.

Full access
Mark A. Snyder
and
Lisa C. Sloan

Abstract

Regional climate models (RCMs) have improved our understanding of the effects of global climate change on specific regions. The need for realistic forcing has led to the use of fully coupled global climate models (GCMs) to produce boundary conditions for RCMs. The advantages of using fully coupled GCM output is that the global-scale interactions of all components of the climate system (ocean, sea ice, land surface, and atmosphere) are considered. This study uses an RCM, driven by a fully coupled GCM, to examine the climate of a region centered over California for the time periods 1980–99 and 2080–99. Statistically significant increases in mean monthly temperatures by up to 7°C are found for the entire state. Large changes in precipitation occur in northern California in February (increase of up to 4 mm day−1 or 30%) and March (decrease of up to 3 mm day−1 or 25%). However, in most months, precipitation changes between the cases were not statistically significant. Statistically significant decreases in snow accumulation of over 100 mm (50%) occur in some months. Temperature increases lead to decreases in snow accumulation that impact the hydrologic budget by shifting spring and summer runoff into the winter months, reinforcing results of other studies that used different models and driving conditions.

Full access
Jason L. Bell
,
Lisa C. Sloan
, and
Mark A. Snyder

Abstract

In this study a regional climate model is employed to expand on modeling experiments of future climate change to address issues of 1) the timing and length of the growing season and 2) the frequency and intensity of extreme temperatures and precipitation. The study focuses on California as a climatically complex region that is vulnerable to changes in water supply and delivery. Statistically significant increases in daily minimum and maximum temperatures occur with a doubling of atmospheric carbon dioxide concentration. Increases in daily temperatures lead to increases in prolonged heat waves and length of the growing season. Changes in total and extreme precipitation vary depending upon geographic location.

Full access
Eric E. Small
,
Filippo Giorgi
,
Lisa Cirbus Sloan
, and
Steven Hostetler

Abstract

Anthropogenic desiccation of the Aral Sea between 1960 and the mid-1990s resulted in a substantial modification of the land surface that changed air temperature in the surrounding region. During the desiccation interval, the net annual rate of precipitation minus evaporation (PE) over the Aral Sea’s surface became more negative by ∼15%, with the greatest changes occurring during the summer months. In addition, Aral Sea surface temperatures (SST) increased by up to 5°C in the spring and summer and decreased by up to 4°C in the fall and winter. A series of coupled regional climate–lake model experiments were completed to evaluate if the observed hydrologic changes are caused by desiccation or instead reflect larger-scale climatic variability or change, or some combination of both. If the PE changes are the result of desiccation, then a positive feedback exists that has amplified the anthropogenic perturbation to the hydrologic system.

The effects of desiccation are examined by varying the simulated area, depth, and salinity of the Aral Sea in different model experiments. The simulated changes in SST resulting from desiccation are similar to the observed changes—both simulated and observed SSTs have increased during the spring and summer and have decreased during the fall and winter. The simulated changes in the annual cycle of PE resulting from desiccation are also similar to observed changes, but the simulated net annual decrease in PE is only ∼30% of the observed decrease. Warming has been observed across central Asia during the desiccation interval. The hydrologic response to this large-scale climatic variability or change was assessed by perturbing the meteorological boundary conditions (1.5°C cooling with constant relative humidity) but leaving the Aral Sea characteristics unchanged. The simulated effects of warming do not closely match the observed changes on the monthly timescale—SST changes are positive and the PE changes are negative in all months. However, the annual change in PE is similar to the observed value.

The simulated hydrologic response to the combined effects of desiccation and warming matches the observed SST and PE changes more closely than the response to each forcing alone. This result indicates that a combination of both desiccation and climatic change or variability has produced the observed hydrological changes—the primary effect of desiccation is to alter the annual cycle of SST and PE whereas warming has modified the hydrologic budget on the annual timescale.

Full access
Eric E. Small
,
Lisa Cirbus Sloan
, and
Doug Nychka

Abstract

A statistical method for establishing the cause–effect relationship between a land surface modification and some component of observed climatic change is presented. This method aids attribution in two ways. First, the climatic changes that are unique to the area influenced by some land surface modification are identified. This isolates changes caused by the spatially restricted forcing from changes caused by other factors. Second, most of the short-term climatic variability in the records from the affected area is removed based on information from the surrounding region. This makes it possible to identify smaller climatic changes. This method is used to identify the changes in surface air temperature that have resulted from desiccation of the Aral Sea (1960–97). Desiccation has weakened the “lake effect” of the Aral Sea, so regional climatic changes are expected.

Substantial temperature trends, unrelated to desiccation, are observed across a broad region of central Asia (∼2000 km) between 1960 and 1997. These trends are similar in magnitude to the changes from desiccation. These trends are removed from the records from the Aral region because they would enhance or offset the local temperature changes caused by desiccation. There is also substantial year-to-year temperature variability that is spatially coherent across central Asia. The method used here removes ∼80%–90% of this short-term variability in the observed temperature records from the Aral region. This lowers the climate change detection limit from ∼3°–8°C to ∼1°–2°C, which improves the identification of the spatial extent of the desiccation-induced changes.

The climate records from around the Aral Sea show dramatic temperature changes between 1960 and 1997, once regionally coherent trends and variability are removed. Mean, maximum, and minimum temperature near the Aral Sea have changed by up to 6°C. Warming (cooling) is observed during spring and summer (autumn and winter), as expected to accompany the diminished lake effect caused by desiccation. The magnitude of temperature changes decreases with increasing distance from the 1960 shoreline, with changes extending up to ∼200 km from the shoreline in the downwind direction. An increase in diurnal temperature range of 2°–3°C is observed in all months, demonstrating a weakening of the lake’s damping effect on the diurnal temperature cycle.

Full access
David W. Pierce
,
Daniel R. Cayan
,
Tapash Das
,
Edwin P. Maurer
,
Norman L. Miller
,
Yan Bao
,
M. Kanamitsu
,
Kei Yoshimura
,
Mark A. Snyder
,
Lisa C. Sloan
,
Guido Franco
, and
Mary Tyree

Abstract

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

Full access
Linda O. Mearns
,
Melissa S. Bukovsky
,
Ruby Leung
,
Yun Qian
,
Ray Arritt
,
William Gutowski
,
Eugene S. Takle
,
Sébastien Biner
,
Daniel Caya
,
James Correia Jr.
,
Richard Jones
,
Lisa Sloan
, and
Mark Snyder
Full access
Linda O. Mearns
,
Ray Arritt
,
Sébastien Biner
,
Melissa S. Bukovsky
,
Seth McGinnis
,
Stephan Sain
,
Daniel Caya
,
James Correia Jr.
,
Dave Flory
,
William Gutowski
,
Eugene S. Takle
,
Richard Jones
,
Ruby Leung
,
Wilfran Moufouma-Okia
,
Larry McDaniel
,
Ana M. B. Nunes
,
Yun Qian
,
John Roads
,
Lisa Sloan
, and
Mark Snyder

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II.

This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.

Full access
Jeremy S. Pal
,
Filippo Giorgi
,
Xunqiang Bi
,
Nellie Elguindi
,
Fabien Solmon
,
Xuejie Gao
,
Sara A. Rauscher
,
Raquel Francisco
,
Ashraf Zakey
,
Jonathan Winter
,
Moetasim Ashfaq
,
Faisal S. Syed
,
Jason L. Bell
,
Noah S. Diffenbaugh
,
Jagadish Karmacharya
,
Abourahamane Konaré
,
Daniel Martinez
,
Rosmeri P. da Rocha
,
Lisa C. Sloan
, and
Allison L. Steiner

Regional climate models are important research tools available to scientists around the world, including in economically developing nations (EDNs). The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a state-of-the-science regional climate model called the ICTP Regional Climate Model version 3 (RegCM3), which is currently being used by a large research community for a diverse range of climate-related studies. The RegCM3 is the central, but not only, tool of the ICTP-maintained Regional Climate Research Network (RegCNET) aimed at creating south–south and north–south scientific interactions on the topic of climate and associated impacts research and modeling. In this paper, RegCNET, RegCM3, and illustrative results from RegCM3 benchmark simulations applied over south Asia, Africa, and South America are presented. It is shown that RegCM3 performs reasonably well over these regions and is therefore useful for climate studies in EDNs.

Full access