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- Author or Editor: Linda O. Mearns x
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Abstract
The “reliability ensemble averaging” (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere–ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two “reliability criteria”: the performance of the model in reproducing present-day climate (“model performance” criterion) and the convergence of the simulated changes across models (“model convergence” criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by ± the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about −25% and +30% (in units of percent of present-day precipitation). The uncertainty range around these changes (± rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of “outlier” or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.
Abstract
The “reliability ensemble averaging” (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere–ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two “reliability criteria”: the performance of the model in reproducing present-day climate (“model performance” criterion) and the convergence of the simulated changes across models (“model convergence” criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by ± the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about −25% and +30% (in units of percent of present-day precipitation). The uncertainty range around these changes (± rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of “outlier” or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.
Abstract
This study assesses the performance of the regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) for the Upper Colorado River basin (UCRB), U.S. Rocky Mountains. The UCRB is a major contributor to the Colorado River’s runoff. Its significant snow-dominated hydrological regime makes it highly sensitive to climatic changes, and future water shortage in this region is likely. The RCMs are evaluated with a clear RCM output user’s perspective and a main focus on snow. Snow water equivalent (SWE) and snow duration, as well as air temperature and precipitation from five RCMs, are compared with snowpack telemetry (SNOTEL) observations, with National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis II (R2), which provides the boundary conditions for the RCM simulations, and with North American Regional Reanalysis (NARR). Overall, most RCMs were able to significantly improve on the results from the NCEP–NCAR reanalysis. However, in comparison with spatially aggregated point observations and NARR, the RCMs are generally too dry, too warm, simulate too little SWE, and have a too-short snow cover duration with a too-late start and a too-early end of a significant snow cover. The intermodel biases found are partly associated with inadequately resolved topography (at the spatial resolution of the RCMs), imperfect observational data, different forcing techniques (spectral nudging versus no nudging), and the different land surface schemes (LSS). Attributing the found biases to specific features of the RCMs remains difficult or even impossible without detailed knowledge of the physical and technical specification of the models.
Abstract
This study assesses the performance of the regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) for the Upper Colorado River basin (UCRB), U.S. Rocky Mountains. The UCRB is a major contributor to the Colorado River’s runoff. Its significant snow-dominated hydrological regime makes it highly sensitive to climatic changes, and future water shortage in this region is likely. The RCMs are evaluated with a clear RCM output user’s perspective and a main focus on snow. Snow water equivalent (SWE) and snow duration, as well as air temperature and precipitation from five RCMs, are compared with snowpack telemetry (SNOTEL) observations, with National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis II (R2), which provides the boundary conditions for the RCM simulations, and with North American Regional Reanalysis (NARR). Overall, most RCMs were able to significantly improve on the results from the NCEP–NCAR reanalysis. However, in comparison with spatially aggregated point observations and NARR, the RCMs are generally too dry, too warm, simulate too little SWE, and have a too-short snow cover duration with a too-late start and a too-early end of a significant snow cover. The intermodel biases found are partly associated with inadequately resolved topography (at the spatial resolution of the RCMs), imperfect observational data, different forcing techniques (spectral nudging versus no nudging), and the different land surface schemes (LSS). Attributing the found biases to specific features of the RCMs remains difficult or even impossible without detailed knowledge of the physical and technical specification of the models.
Abstract
An analysis of detailed relationships between the North Atlantic Oscillation–Arctic Oscillation (NAO–AO) and local temperature response throughout the northeastern United States and neighboring areas of Canada is presented. In particular, the study focuses on how contrasts in the mean and daily variance, based on AO phase, are associated with contrasts in the frequency and intensity of extreme temperature events in both winter and spring. In this region, notable contrasts in mean temperatures in winter and daily variance in spring, which influence the pattern of extremes, are associated with phases of the NAO–AO. Warmer temperatures in New England and cooler temperatures in Quebec, Canada, result during winter with increases in the NAO–AO index. The mean temperature response is weaker in spring, but the response of daily variance of temperature is stronger; variance increases with the NAO–AO index. Relationships between these effects help explain significant increases in maximum temperature extremes during winter in New England and in minimum temperature extremes during spring in Quebec for high NAO–AO index years. Diurnal temperature range tends to be larger in AO-positive winters and springs throughout the region. This study helps put other work on the trends in regional extreme events into the context of large-scale climate variability.
Abstract
An analysis of detailed relationships between the North Atlantic Oscillation–Arctic Oscillation (NAO–AO) and local temperature response throughout the northeastern United States and neighboring areas of Canada is presented. In particular, the study focuses on how contrasts in the mean and daily variance, based on AO phase, are associated with contrasts in the frequency and intensity of extreme temperature events in both winter and spring. In this region, notable contrasts in mean temperatures in winter and daily variance in spring, which influence the pattern of extremes, are associated with phases of the NAO–AO. Warmer temperatures in New England and cooler temperatures in Quebec, Canada, result during winter with increases in the NAO–AO index. The mean temperature response is weaker in spring, but the response of daily variance of temperature is stronger; variance increases with the NAO–AO index. Relationships between these effects help explain significant increases in maximum temperature extremes during winter in New England and in minimum temperature extremes during spring in Quebec for high NAO–AO index years. Diurnal temperature range tends to be larger in AO-positive winters and springs throughout the region. This study helps put other work on the trends in regional extreme events into the context of large-scale climate variability.
Abstract
The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.
Abstract
The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.
Abstract
Most climate impact studies rely on changes in means of meteorological variables, such as temperature, to estimate potential climate impacts, including effects on agricultural production. However, extreme meteorological events, say, a short period of abnormally high temperatures, can have a significant harmful effect on crop growth and final yield. The characteristics of daily temperature time series, specifically mean, variance and autocorrelation, are analyzed to determine possible ranges of probabilities of certain extreme temperature events [e.g., runs of consecutive daily maximum temperatures of at least 95°F (35°C)] with changes in mean temperature of the time series. The extreme temperature events considered are motivated primarily by agricultural concerns, particularly, the effects of high temperatures on corn yields in the U.S. Corn Belt. However, runs of high temperatures can also affect, for example, energy demand or morbidity and mortality of animals and humans.
The relationships between changes in mean temperature and the corresponding changes in the probabilities of these extreme temperature events are quite nonlinear, with relatively small changes in mean temperature sometimes resulting in relatively large changes in event probabilities. In particular, the likelihood of occurrence of a run of five consecutive daily maximum temperatures of at least 95°F under a 3°F (1.7°C) increase in the mean (holding the variance and autocorrelation constant) is about three times greater than that under the current climate at Des Moines, Moreover, by allowing either the variance or the autocorrelation as well as the mean to change, this likelihood of a run event varies over a relatively wide range of values. These changes in the probabilities of extreme events need to be taken into consideration in order to obtain realistic estimates of the impact of climate changes such as increases in mean temperature that may arise from increases in atmospheric carbon dioxide concentration.
Abstract
Most climate impact studies rely on changes in means of meteorological variables, such as temperature, to estimate potential climate impacts, including effects on agricultural production. However, extreme meteorological events, say, a short period of abnormally high temperatures, can have a significant harmful effect on crop growth and final yield. The characteristics of daily temperature time series, specifically mean, variance and autocorrelation, are analyzed to determine possible ranges of probabilities of certain extreme temperature events [e.g., runs of consecutive daily maximum temperatures of at least 95°F (35°C)] with changes in mean temperature of the time series. The extreme temperature events considered are motivated primarily by agricultural concerns, particularly, the effects of high temperatures on corn yields in the U.S. Corn Belt. However, runs of high temperatures can also affect, for example, energy demand or morbidity and mortality of animals and humans.
The relationships between changes in mean temperature and the corresponding changes in the probabilities of these extreme temperature events are quite nonlinear, with relatively small changes in mean temperature sometimes resulting in relatively large changes in event probabilities. In particular, the likelihood of occurrence of a run of five consecutive daily maximum temperatures of at least 95°F under a 3°F (1.7°C) increase in the mean (holding the variance and autocorrelation constant) is about three times greater than that under the current climate at Des Moines, Moreover, by allowing either the variance or the autocorrelation as well as the mean to change, this likelihood of a run event varies over a relatively wide range of values. These changes in the probabilities of extreme events need to be taken into consideration in order to obtain realistic estimates of the impact of climate changes such as increases in mean temperature that may arise from increases in atmospheric carbon dioxide concentration.
Abstract
A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where model results agree (or outlying projections are discounted) to multimodal curves where models that cannot be discounted on the basis of bias give diverging projections. Besides the basic statistical model, the authors consider including correlation between present and future temperature responses, and test alternative forms of probability distributions for the model error terms. It is suggested that a probabilistic approach, particularly in the form of a Bayesian model, is a useful platform from which to synthesize the information from an ensemble of simulations. The probability distributions of temperature change reveal features such as multimodality and long tails that could not otherwise be easily discerned. Furthermore, the Bayesian model can serve as an interdisciplinary tool through which climate modelers, climatologists, and statisticians can work more closely. For example, climate modelers, through their expert judgment, could contribute to the formulations of prior distributions in the statistical model.
Abstract
A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where model results agree (or outlying projections are discounted) to multimodal curves where models that cannot be discounted on the basis of bias give diverging projections. Besides the basic statistical model, the authors consider including correlation between present and future temperature responses, and test alternative forms of probability distributions for the model error terms. It is suggested that a probabilistic approach, particularly in the form of a Bayesian model, is a useful platform from which to synthesize the information from an ensemble of simulations. The probability distributions of temperature change reveal features such as multimodality and long tails that could not otherwise be easily discerned. Furthermore, the Bayesian model can serve as an interdisciplinary tool through which climate modelers, climatologists, and statisticians can work more closely. For example, climate modelers, through their expert judgment, could contribute to the formulations of prior distributions in the statistical model.
Abstract
The authors examine 17 dynamically downscaled simulations produced as part of the North American Regional Climate Change Assessment Program (NARCCAP) for their skill in reproducing the North American monsoon system. The focus is on precipitation and the drivers behind the precipitation biases seen in the simulations of the current climate. Thus, a process-based approach to the question of model fidelity is taken in order to help assess confidence in this suite of simulations.
The results show that the regional climate models (RCMs) forced with a reanalysis product and atmosphere-only global climate model (AGCM) time-slice simulations perform reasonably well over the core Mexican and southwest United States regions. Some of the dynamically downscaled simulations do, however, have strong dry biases in Arizona that are related to their inability to develop credible monsoon flow structure over the Gulf of California. When forced with different atmosphere–ocean coupled global climate models (AOGCMs) for the current period, the skill of the RCMs subdivides largely by the skill of the forcing or “parent” AOGCM. How the inherited biases affect the RCM simulations is investigated. While it is clear that the AOGCMs have a large influence on the RCMs, the authors also demonstrate where the regional models add value to the simulations and discuss the differential credibility of the six RCMs (17 total simulations), two AGCM time slices, and four AOGCMs examined herein. It is found that in-depth analysis of parent GCM and RCM scenarios can identify a meaningful subset of models that can produce credible simulations of the North American monsoon precipitation.
Abstract
The authors examine 17 dynamically downscaled simulations produced as part of the North American Regional Climate Change Assessment Program (NARCCAP) for their skill in reproducing the North American monsoon system. The focus is on precipitation and the drivers behind the precipitation biases seen in the simulations of the current climate. Thus, a process-based approach to the question of model fidelity is taken in order to help assess confidence in this suite of simulations.
The results show that the regional climate models (RCMs) forced with a reanalysis product and atmosphere-only global climate model (AGCM) time-slice simulations perform reasonably well over the core Mexican and southwest United States regions. Some of the dynamically downscaled simulations do, however, have strong dry biases in Arizona that are related to their inability to develop credible monsoon flow structure over the Gulf of California. When forced with different atmosphere–ocean coupled global climate models (AOGCMs) for the current period, the skill of the RCMs subdivides largely by the skill of the forcing or “parent” AOGCM. How the inherited biases affect the RCM simulations is investigated. While it is clear that the AOGCMs have a large influence on the RCMs, the authors also demonstrate where the regional models add value to the simulations and discuss the differential credibility of the six RCMs (17 total simulations), two AGCM time slices, and four AOGCMs examined herein. It is found that in-depth analysis of parent GCM and RCM scenarios can identify a meaningful subset of models that can produce credible simulations of the North American monsoon precipitation.
Abstract
The authors examine the effect of seasonal crop development and growth on the warm-season mesoscale heat, moisture, and momentum fluxes over the central Great Plains region of North America. The effect of crop growth and development on the atmospheric boundary layer is addressed in a follow-up paper (Part II). Energy, moisture, and momentum fluxes are studied over a maize agroecosystem at the scale of a 90-km atmospheric grid cell. Daily plant development and growth functions incorporated into the surface flux calculations are based on a physiological crop growth model CERES-Maize version 3.0. CERES-Maize simulates daily plant growth and development as a function of both environmental conditions (temperature, precipitation, solar radiation, and soil moisture) and plant-specific genetic parameters. Plant growth and development functions from CERES were incorporated into the Biosphere–Atmosphere Transfer Scheme (BATS), and selected crop parameters [i.e., Leaf Area Index (LAI) and crop height] were validated against field data. The sensitivity of sensible (H) and latent (LE) heat fluxes, and momentum flux (τ) to interactively simulated LAI and canopy height was quantified.
During the extremely dry season of 1988, 20%–35% changes in sensible heat and 30%–45% changes in latent heat occurred in response to LAI changes from 5 to 1 (the values simulated in the control and interactive experiments, respectively). These changes are statistically significant (at the 0.05 level) for all the locations and years under consideration. Relative contributions of evaporation and transpiration to the latent heat flux were also strongly affected by these LAI changes. This effect had a distinct diurnal pattern, with the strongest signal seen in midafternoon hours, and was more pronounced during the dry years (e.g., 1988 and 1989) compared to the favorably moist years (e.g., 1991, 1993).
Abstract
The authors examine the effect of seasonal crop development and growth on the warm-season mesoscale heat, moisture, and momentum fluxes over the central Great Plains region of North America. The effect of crop growth and development on the atmospheric boundary layer is addressed in a follow-up paper (Part II). Energy, moisture, and momentum fluxes are studied over a maize agroecosystem at the scale of a 90-km atmospheric grid cell. Daily plant development and growth functions incorporated into the surface flux calculations are based on a physiological crop growth model CERES-Maize version 3.0. CERES-Maize simulates daily plant growth and development as a function of both environmental conditions (temperature, precipitation, solar radiation, and soil moisture) and plant-specific genetic parameters. Plant growth and development functions from CERES were incorporated into the Biosphere–Atmosphere Transfer Scheme (BATS), and selected crop parameters [i.e., Leaf Area Index (LAI) and crop height] were validated against field data. The sensitivity of sensible (H) and latent (LE) heat fluxes, and momentum flux (τ) to interactively simulated LAI and canopy height was quantified.
During the extremely dry season of 1988, 20%–35% changes in sensible heat and 30%–45% changes in latent heat occurred in response to LAI changes from 5 to 1 (the values simulated in the control and interactive experiments, respectively). These changes are statistically significant (at the 0.05 level) for all the locations and years under consideration. Relative contributions of evaporation and transpiration to the latent heat flux were also strongly affected by these LAI changes. This effect had a distinct diurnal pattern, with the strongest signal seen in midafternoon hours, and was more pronounced during the dry years (e.g., 1988 and 1989) compared to the favorably moist years (e.g., 1991, 1993).
