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- Author or Editor: Chad W. Thackeray x
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Abstract
Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.
Abstract
Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.
Abstract
Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R 2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.
Abstract
Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R 2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.
Abstract
Large uncertainty exists in hydrologic sensitivity (HS), the global-mean precipitation increase per degree of warming, across global climate model (GCM) ensembles. Meanwhile, the global circulation and hence global precipitation are sensitive to variations of surface temperature under internal variability. El Niño–Southern Oscillation (ENSO) is the most dominant mode of global temperature variability and hence of precipitation variability. Here we show in phase 6 of the Coupled Model Intercomparison Project (CMIP6) that the strength of HS under ENSO is predictive of HS in the climate change context (r = 0.56). This correlation increases to 0.62 when only central Pacific ENSO events are considered, suggesting that they are a better proxy for HS under future warming than east Pacific ENSO events. GCMs with greater HS are associated with greater weakening of the Walker circulation and expansion of the Hadley circulation under ENSO. Observations of HS under ENSO suggest that it is significantly underestimated by the GCMs, with the lower bound of observational uncertainty almost double even the highest-HS GCMs. The ENSO-related transformation of the tropical circulation holds clues into how the GCMs may be improved in order to more reliably simulate future hydrological cycle intensification.
Abstract
Large uncertainty exists in hydrologic sensitivity (HS), the global-mean precipitation increase per degree of warming, across global climate model (GCM) ensembles. Meanwhile, the global circulation and hence global precipitation are sensitive to variations of surface temperature under internal variability. El Niño–Southern Oscillation (ENSO) is the most dominant mode of global temperature variability and hence of precipitation variability. Here we show in phase 6 of the Coupled Model Intercomparison Project (CMIP6) that the strength of HS under ENSO is predictive of HS in the climate change context (r = 0.56). This correlation increases to 0.62 when only central Pacific ENSO events are considered, suggesting that they are a better proxy for HS under future warming than east Pacific ENSO events. GCMs with greater HS are associated with greater weakening of the Walker circulation and expansion of the Hadley circulation under ENSO. Observations of HS under ENSO suggest that it is significantly underestimated by the GCMs, with the lower bound of observational uncertainty almost double even the highest-HS GCMs. The ENSO-related transformation of the tropical circulation holds clues into how the GCMs may be improved in order to more reliably simulate future hydrological cycle intensification.
Abstract
An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.
Abstract
An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.
Abstract
Daily and subdaily precipitation extremes in historical phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01 to 10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes, the multimodel median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r = −0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r = −0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These intermodel differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible twenty-first-century projections.
Abstract
Daily and subdaily precipitation extremes in historical phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01 to 10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes, the multimodel median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r = −0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r = −0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These intermodel differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible twenty-first-century projections.
