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- Author or Editor: Garry K. C. Clarke x
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Abstract
The authors analyze the performance of 22 Intergovernmental Panel on Climate Change (IPCC) global climate models (GCMs) over all of North America and its western subregion using several different evaluation metrics. They assess the model skill in simulating climatologies of several climate variables and the skill in simulating the daily synoptic patterns. The evaluation is performed by comparing the model output with the North American Regional Reanalysis (NARR) over the period 1980–99. One set of metrics, based on root-mean-square errors and variance ratios, compares modeled versus the NARR mean annual cycle and interannual variability. Based on these measures the three top performing models are the ECHAM5–Max Planck Institute Ocean Model (MPI-OM), the third climate configuration of the Met Office Unified Model (HadCM3), and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 [CGCM3.1(T47)]. Models that perform well over all North America also perform well over its western subregion. However, the model ranking is sensitive to the choice of climate variable. For another evaluation measure the method of self-organizing maps was applied to classify the characteristic daily patterns of sea level pressure over the region. The evaluation consists of correlating the frequencies of these patterns, as generated in GCMs, with the frequencies in the NARR over the baseline period. Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern. However, very few GCMs are able to reproduce the occurrences of characteristic daily weather patterns in the NARR on seasonal basis over the baseline period. In terms of relative performance, the three top performing models are the Meteorological Research Institute (MRI) CGCM2.3.2, ECHAM5–MPI-OM, and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]. The model skill in simulating daily synoptic patterns is not strongly linked to the skill in simulating the climatologies of selected variables. Despite the large scatter of model performance across all the metrics, some models consistently rank high [e.g., ECHAM5–MPI-OM and MIROC3.2(medres)]. Likewise, some models consistently rank low [e.g., the Community Climate System Model, version 3 (CCSM3) and the Goddard Institute for Space Studies Model E-R (GISS-ER)] independently of the evaluation measures, domain size, and climate variable of interest.
Abstract
The authors analyze the performance of 22 Intergovernmental Panel on Climate Change (IPCC) global climate models (GCMs) over all of North America and its western subregion using several different evaluation metrics. They assess the model skill in simulating climatologies of several climate variables and the skill in simulating the daily synoptic patterns. The evaluation is performed by comparing the model output with the North American Regional Reanalysis (NARR) over the period 1980–99. One set of metrics, based on root-mean-square errors and variance ratios, compares modeled versus the NARR mean annual cycle and interannual variability. Based on these measures the three top performing models are the ECHAM5–Max Planck Institute Ocean Model (MPI-OM), the third climate configuration of the Met Office Unified Model (HadCM3), and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 [CGCM3.1(T47)]. Models that perform well over all North America also perform well over its western subregion. However, the model ranking is sensitive to the choice of climate variable. For another evaluation measure the method of self-organizing maps was applied to classify the characteristic daily patterns of sea level pressure over the region. The evaluation consists of correlating the frequencies of these patterns, as generated in GCMs, with the frequencies in the NARR over the baseline period. Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern. However, very few GCMs are able to reproduce the occurrences of characteristic daily weather patterns in the NARR on seasonal basis over the baseline period. In terms of relative performance, the three top performing models are the Meteorological Research Institute (MRI) CGCM2.3.2, ECHAM5–MPI-OM, and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]. The model skill in simulating daily synoptic patterns is not strongly linked to the skill in simulating the climatologies of selected variables. Despite the large scatter of model performance across all the metrics, some models consistently rank high [e.g., ECHAM5–MPI-OM and MIROC3.2(medres)]. Likewise, some models consistently rank low [e.g., the Community Climate System Model, version 3 (CCSM3) and the Goddard Institute for Space Studies Model E-R (GISS-ER)] independently of the evaluation measures, domain size, and climate variable of interest.
Abstract
In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures––in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901–2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979–2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU–NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU–NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU.
Abstract
In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures––in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901–2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979–2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU–NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU–NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU.
Abstract
To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.
Abstract
To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.
Abstract
A cold event at around 8200 calendar years BP and the release, at around that time, of a huge freshwater outburst from ice-dammed glacial Lake Agassiz have lent support to the idea that the flood triggered the cold event. Some suggest that the freshwater addition caused a weakening of the North Atlantic meridional overturning circulation (MOC) thereby reducing the ocean transport of heat to high northern latitudes. Although several modeling efforts lend strength to this claim, the paleoceanographic record is equivocal. The authors’ aim is to use a coupled ocean–atmosphere model to examine the possibility that the two events are causally linked but that MOC reduction was not the main agent of change. It is found that the outburst flood and associated redirection of postflood meltwater drainage to the Labrador Sea, via Hudson Strait, can freshen the North Atlantic, leading to reduced salinity and sea surface temperature, and thus to increased sea ice production at high latitudes. The results point to the possibility that the preflood outflow to the St. Lawrence was extremely turbid and sufficiently dense to become hyperpycnal, whereas the postflood outflow through Hudson Strait had a lower load of suspended sediment and was buoyant.
Abstract
A cold event at around 8200 calendar years BP and the release, at around that time, of a huge freshwater outburst from ice-dammed glacial Lake Agassiz have lent support to the idea that the flood triggered the cold event. Some suggest that the freshwater addition caused a weakening of the North Atlantic meridional overturning circulation (MOC) thereby reducing the ocean transport of heat to high northern latitudes. Although several modeling efforts lend strength to this claim, the paleoceanographic record is equivocal. The authors’ aim is to use a coupled ocean–atmosphere model to examine the possibility that the two events are causally linked but that MOC reduction was not the main agent of change. It is found that the outburst flood and associated redirection of postflood meltwater drainage to the Labrador Sea, via Hudson Strait, can freshen the North Atlantic, leading to reduced salinity and sea surface temperature, and thus to increased sea ice production at high latitudes. The results point to the possibility that the preflood outflow to the St. Lawrence was extremely turbid and sufficiently dense to become hyperpycnal, whereas the postflood outflow through Hudson Strait had a lower load of suspended sediment and was buoyant.
Abstract
A method is described to estimate the thickness of glacier ice using information derived from the measured ice extent, surface topography, surface mass balance, and rate of thinning or thickening of the ice column. Shear stress beneath an ice column is assumed to be simply related to ice thickness and surface slope, as for an inclined slab, but this calculation is cast as a linear optimization problem so that a smoothness regularization can be applied. Assignment of bed stress is based on the flow law for ice and a mass balance calculation but must be preceded by delineation of the ice flow drainage basin. Validation of the method is accomplished by comparing thickness estimates to the known thickness generated by a numerical ice dynamics model. Once validated, the method is used to estimate the subglacial topography for all glaciers in western Canada that lie south of 60°N. Adding the present ice volume of each glacier gives the estimated total volume as 2320 km3, equivalent to 5.8 mm of sea level rise. Taking the glaciated area as 26 590 km2 gives the average glacier thickness as 87.2 m. A detailed error analysis indicates that systematic errors are likely to increase the estimated sea level rise and when random errors are included the combined result is 6.3 ± 0.6 mm or, expressed as ice volume, 2530 ± 220 km3.
Abstract
A method is described to estimate the thickness of glacier ice using information derived from the measured ice extent, surface topography, surface mass balance, and rate of thinning or thickening of the ice column. Shear stress beneath an ice column is assumed to be simply related to ice thickness and surface slope, as for an inclined slab, but this calculation is cast as a linear optimization problem so that a smoothness regularization can be applied. Assignment of bed stress is based on the flow law for ice and a mass balance calculation but must be preceded by delineation of the ice flow drainage basin. Validation of the method is accomplished by comparing thickness estimates to the known thickness generated by a numerical ice dynamics model. Once validated, the method is used to estimate the subglacial topography for all glaciers in western Canada that lie south of 60°N. Adding the present ice volume of each glacier gives the estimated total volume as 2320 km3, equivalent to 5.8 mm of sea level rise. Taking the glaciated area as 26 590 km2 gives the average glacier thickness as 87.2 m. A detailed error analysis indicates that systematic errors are likely to increase the estimated sea level rise and when random errors are included the combined result is 6.3 ± 0.6 mm or, expressed as ice volume, 2530 ± 220 km3.
Abstract
The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.
Abstract
The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.
