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Abstract
Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of the separate roles of temperature and precipitation in determining the trends in SWE; 2) an estimation of the sensitivity of SWE to warming trends, and its distribution across western North America and as a function of elevation; and 3) inferences about responses of SWE to future warming. These results emphasize the sensitivity to warming of the mountains of northern California and the Cascades of Oregon and Washington. In addition, the contribution of modes of Pacific climate variability is examined and found to be responsible for about 10%–60% of the trends in SWE, depending on the period of record and climate index.
Abstract
Records of 1 April snow water equivalent (SWE) are examined here using multiple linear regression against reference time series of temperature and precipitation. This method permits 1) an examination of the separate roles of temperature and precipitation in determining the trends in SWE; 2) an estimation of the sensitivity of SWE to warming trends, and its distribution across western North America and as a function of elevation; and 3) inferences about responses of SWE to future warming. These results emphasize the sensitivity to warming of the mountains of northern California and the Cascades of Oregon and Washington. In addition, the contribution of modes of Pacific climate variability is examined and found to be responsible for about 10%–60% of the trends in SWE, depending on the period of record and climate index.
Abstract
In common with much of the western United States, the Pacific Northwest (defined in this paper as Washington and Oregon) has experienced an unusual number of droughts in the past decade. This paper describes three of these droughts in terms of the precipitation, temperature, and soil moisture anomalies, and discusses different drought impacts experienced in the Pacific Northwest (PNW). For the first drought, in 2001, low winter precipitation in the PNW produced very low streamflow that primarily affected farmers and hydropower generation. For the second, in 2003, low summer precipitation in Washington (WA), and low summer precipitation and a warm winter in Oregon (OR) primarily affected streamflow and forests. For the last, in 2005, a lack of snowpack due to warm temperatures during significant winter precipitation events in WA, and low winter precipitation in OR, had a variety of different agricultural and hydrologic impacts. Although the proximal causes of droughts are easily quantified, the ultimate causes are not as clear. Better precipitation observations in the PNW are required to provide timely monitoring of conditions leading to droughts to improve prediction in the future.
Abstract
In common with much of the western United States, the Pacific Northwest (defined in this paper as Washington and Oregon) has experienced an unusual number of droughts in the past decade. This paper describes three of these droughts in terms of the precipitation, temperature, and soil moisture anomalies, and discusses different drought impacts experienced in the Pacific Northwest (PNW). For the first drought, in 2001, low winter precipitation in the PNW produced very low streamflow that primarily affected farmers and hydropower generation. For the second, in 2003, low summer precipitation in Washington (WA), and low summer precipitation and a warm winter in Oregon (OR) primarily affected streamflow and forests. For the last, in 2005, a lack of snowpack due to warm temperatures during significant winter precipitation events in WA, and low winter precipitation in OR, had a variety of different agricultural and hydrologic impacts. Although the proximal causes of droughts are easily quantified, the ultimate causes are not as clear. Better precipitation observations in the PNW are required to provide timely monitoring of conditions leading to droughts to improve prediction in the future.
Abstract
A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.
Abstract
A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.
As computing power has increased, it has become possible to run a state-of-the-art climate model on a workstation, and the use of climate models is spreading rapidly. However, the dispersion of climate modeling know-how has not kept pace with the dispersion of climate modeling capabilities. To connect new modelers with modeling know-how, a two-week climate modeling workshop was held. Participants designed and ran climate experiments under the guidance of a group of modeling experts. This paper describes the process that was followed in these climate experiments and gives an example of one experiment.
As computing power has increased, it has become possible to run a state-of-the-art climate model on a workstation, and the use of climate models is spreading rapidly. However, the dispersion of climate modeling know-how has not kept pace with the dispersion of climate modeling capabilities. To connect new modelers with modeling know-how, a two-week climate modeling workshop was held. Participants designed and ran climate experiments under the guidance of a group of modeling experts. This paper describes the process that was followed in these climate experiments and gives an example of one experiment.
Abstract
The authors have used a spectral, primitive equation mechanistic model of the stratosphere and mesosphere to simulate observed stratospheric flow through the winters of 1991–92 and 1994–95 by forcing the model at 100 hPa with observed geopotential height. The authors assess the model’s performance quantitatively by comparing the simulations with the United Kingdom Meteorological Office (UKMO) assimilated stratosphere–troposphere data. Time-mean, zonal-mean temperatures are generally within 5 K and winds within 5 m s−1; transient features, such as wave growth, are mostly simulated well. The phase accuracy of planetary-scale waves declines with altitude and wavenumber, and the model has difficulty correctly simulating traveling anticyclones in the upper stratosphere. The authors examine the minor warming of January 1995 which was unusual in its depth and development and which the model simulated fairly well. The authors also examine the minor warming of January 1992, which the model missed, and a major warming in February 1992 that occurred in the model but not in the observations.
Abstract
The authors have used a spectral, primitive equation mechanistic model of the stratosphere and mesosphere to simulate observed stratospheric flow through the winters of 1991–92 and 1994–95 by forcing the model at 100 hPa with observed geopotential height. The authors assess the model’s performance quantitatively by comparing the simulations with the United Kingdom Meteorological Office (UKMO) assimilated stratosphere–troposphere data. Time-mean, zonal-mean temperatures are generally within 5 K and winds within 5 m s−1; transient features, such as wave growth, are mostly simulated well. The phase accuracy of planetary-scale waves declines with altitude and wavenumber, and the model has difficulty correctly simulating traveling anticyclones in the upper stratosphere. The authors examine the minor warming of January 1995 which was unusual in its depth and development and which the model simulated fairly well. The authors also examine the minor warming of January 1992, which the model missed, and a major warming in February 1992 that occurred in the model but not in the observations.
Abstract
Observed changes in climate of the U.S. Pacific Northwest since the early twentieth century were examined using four different datasets. Annual mean temperature increased by approximately 0.6°–0.8°C from 1901 to 2012, with corroborating indicators including a lengthened freeze-free season, increased temperature of the coldest night of the year, and increased growing-season potential evapotranspiration. Seasonal temperature trends over shorter time scales (<50 yr) were variable. Despite increased warming rates in most seasons over the last half century, nonsignificant cooling was observed during spring from 1980 to 2012. Observations show a long-term increase in spring precipitation; however, decreased summer and autumn precipitation and increased potential evapotranspiration have resulted in larger climatic water deficits over the past four decades. A bootstrapped multiple linear regression model was used to better resolve the temporal heterogeneity of seasonal temperature and precipitation trends and to apportion trends to internal climate variability, solar variability, volcanic aerosols, and anthropogenic forcing. The El Niño–Southern Oscillation and the Pacific–North American pattern were the primary modulators of seasonal temperature trends on multidecadal time scales: solar and volcanic forcing were nonsignificant predictors and contributed weakly to observed trends. Anthropogenic forcing was a significant predictor of, and the leading contributor to, long-term warming; natural factors alone fail to explain the observed warming. Conversely, poor model skill for seasonal precipitation suggests that other factors need to be considered to understand the sources of seasonal precipitation trends.
Abstract
Observed changes in climate of the U.S. Pacific Northwest since the early twentieth century were examined using four different datasets. Annual mean temperature increased by approximately 0.6°–0.8°C from 1901 to 2012, with corroborating indicators including a lengthened freeze-free season, increased temperature of the coldest night of the year, and increased growing-season potential evapotranspiration. Seasonal temperature trends over shorter time scales (<50 yr) were variable. Despite increased warming rates in most seasons over the last half century, nonsignificant cooling was observed during spring from 1980 to 2012. Observations show a long-term increase in spring precipitation; however, decreased summer and autumn precipitation and increased potential evapotranspiration have resulted in larger climatic water deficits over the past four decades. A bootstrapped multiple linear regression model was used to better resolve the temporal heterogeneity of seasonal temperature and precipitation trends and to apportion trends to internal climate variability, solar variability, volcanic aerosols, and anthropogenic forcing. The El Niño–Southern Oscillation and the Pacific–North American pattern were the primary modulators of seasonal temperature trends on multidecadal time scales: solar and volcanic forcing were nonsignificant predictors and contributed weakly to observed trends. Anthropogenic forcing was a significant predictor of, and the leading contributor to, long-term warming; natural factors alone fail to explain the observed warming. Conversely, poor model skill for seasonal precipitation suggests that other factors need to be considered to understand the sources of seasonal precipitation trends.
Abstract
No abstract available.
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No abstract available.
Abstract
This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasons.
Abstract
This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasons.
Abstract
A physically based hydrology model is used to produce time series for the period 1916–2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947–2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947–2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December–February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends.
Abstract
A physically based hydrology model is used to produce time series for the period 1916–2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947–2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947–2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December–February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends.
Abstract
Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.
Abstract
Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.
