Abstract
Understanding climate variability from millennial to glacial–interglacial time scales remains challenging due to the complex and nonlinear feedbacks between ice, ocean, sediments, biosphere, and atmosphere. Complex climate models generally struggle to dynamically and comprehensively simulate such long time periods as a result of the large computational costs. Here, we therefore coupled a dynamical ice sheet model to the Bern3D Earth system model of intermediate complexity, which allows for simulating multiple glacial–interglacial cycles. The performance of the model is first validated against modern observations and its response to abrupt perturbations, such as atmospheric CO2 changes and North Atlantic freshwater hosing, is investigated. To further test the fully coupled model, the climate evolution over the entire last glacial cycle is explored in a transient simulation forced by variations in the orbital configuration and greenhouse gases and aerosols. The model simulates global mean surface temperature in fair agreement with reconstructions, exhibiting a gradual cooling trend since the last interglacial that is interrupted by two more rapid cooling events during the early Marine Isotope Stage (MIS) 4 and Last Glacial Maximum (LGM). Simulated Northern Hemispheric ice sheets show pronounced variability on orbital time scales, and ice volume more than doubles from MIS3 to the LGM in good agreement with recent sea level reconstructions. At the LGM, the Atlantic overturning has a strength of about 14 Sv (1 Sv ≡ 106 m3 s−1), which is a reduction by about one-quarter compared to the preindustrial. We thus demonstrate that the new coupled model is able to simulate large-scale aspects of glacial–interglacial cycles.
Abstract
Understanding climate variability from millennial to glacial–interglacial time scales remains challenging due to the complex and nonlinear feedbacks between ice, ocean, sediments, biosphere, and atmosphere. Complex climate models generally struggle to dynamically and comprehensively simulate such long time periods as a result of the large computational costs. Here, we therefore coupled a dynamical ice sheet model to the Bern3D Earth system model of intermediate complexity, which allows for simulating multiple glacial–interglacial cycles. The performance of the model is first validated against modern observations and its response to abrupt perturbations, such as atmospheric CO2 changes and North Atlantic freshwater hosing, is investigated. To further test the fully coupled model, the climate evolution over the entire last glacial cycle is explored in a transient simulation forced by variations in the orbital configuration and greenhouse gases and aerosols. The model simulates global mean surface temperature in fair agreement with reconstructions, exhibiting a gradual cooling trend since the last interglacial that is interrupted by two more rapid cooling events during the early Marine Isotope Stage (MIS) 4 and Last Glacial Maximum (LGM). Simulated Northern Hemispheric ice sheets show pronounced variability on orbital time scales, and ice volume more than doubles from MIS3 to the LGM in good agreement with recent sea level reconstructions. At the LGM, the Atlantic overturning has a strength of about 14 Sv (1 Sv ≡ 106 m3 s−1), which is a reduction by about one-quarter compared to the preindustrial. We thus demonstrate that the new coupled model is able to simulate large-scale aspects of glacial–interglacial cycles.
Abstract
Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.
Abstract
Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.
Abstract
The weather and climate greatly affect the socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation and insurance. It becomes evident that weather and ocean forecasting is high value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.
Abstract
The weather and climate greatly affect the socioeconomic activities on multiple temporal and spatial scales. From a climate perspective, atmospheric and ocean characteristics have determined the life, evolution and prosperity of humans and other species in different areas of the world. On smaller scales, the atmospheric and sea conditions affect various sectors such as civil protection, food security, communications, transportation and insurance. It becomes evident that weather and ocean forecasting is high value information highlighting the need for state-of-the-art forecasting systems to be adopted. This importance has been acknowledged by the authorities of Saudi Arabia entrusting the National Center for Meteorology (NCM) to provide high quality weather and climate analytics. This led to the development of a numerical weather prediction (NWP) system. The new system includes weather, wave and ocean circulation components and has been operational since 2020 enhancing the national capabilities in NWP. Within this article, a description of the system and its performance is discussed alongside future goals.
Abstract
The mechanisms regulating the relationship between the tropical island diurnal cycle and large-scale modes of tropical variability such as the boreal summer intraseasonal oscillation (BSISO) are explored in observations and an idealized model. Specifically, the local environmental conditions associated with diurnal cycle variability are explored. Using Luzon Island in the northern Philippines as an observational test case, a novel probabilistic framework is applied to improve the understanding of diurnal cycle variability. High-amplitude diurnal cycle days tend to occur with weak to moderate offshore low-level wind and near to above average column moisture in the local environment. The transition from the BSISO suppressed phase to the active phase is most likely to produce the wind and moisture conditions supportive of a substantial diurnal cycle over western Luzon and the South China Sea (SCS). Thus, the impact of the BSISO on the local diurnal cycle can be understood in terms of the change in the probability of favorable environmental conditions. Idealized high-resolution 3D Cloud Model 1 (CM1) simulations driven by base states derived from BSISO composite profiles are able to reproduce several important features of the observed diurnal cycle variability with BSISO phase, including the strong, land-based diurnal cycle and offshore propagation in the transition phases. Background wind appears to be the primary variable controlling the diurnal cycle response, but ambient moisture distinctly reduces precipitation strength in the suppressed BSISO phase and enhances it in the active phase.
Abstract
The mechanisms regulating the relationship between the tropical island diurnal cycle and large-scale modes of tropical variability such as the boreal summer intraseasonal oscillation (BSISO) are explored in observations and an idealized model. Specifically, the local environmental conditions associated with diurnal cycle variability are explored. Using Luzon Island in the northern Philippines as an observational test case, a novel probabilistic framework is applied to improve the understanding of diurnal cycle variability. High-amplitude diurnal cycle days tend to occur with weak to moderate offshore low-level wind and near to above average column moisture in the local environment. The transition from the BSISO suppressed phase to the active phase is most likely to produce the wind and moisture conditions supportive of a substantial diurnal cycle over western Luzon and the South China Sea (SCS). Thus, the impact of the BSISO on the local diurnal cycle can be understood in terms of the change in the probability of favorable environmental conditions. Idealized high-resolution 3D Cloud Model 1 (CM1) simulations driven by base states derived from BSISO composite profiles are able to reproduce several important features of the observed diurnal cycle variability with BSISO phase, including the strong, land-based diurnal cycle and offshore propagation in the transition phases. Background wind appears to be the primary variable controlling the diurnal cycle response, but ambient moisture distinctly reduces precipitation strength in the suppressed BSISO phase and enhances it in the active phase.
Abstract
Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.
Significance Statement
Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.
Abstract
Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.
Significance Statement
Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low-resolution (LR) or high-resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5 reanalysis. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger seas. Supporting previous studies, the Labrador–Irminger seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic Meridional Overturning Circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low-resolution (LR) or high-resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5 reanalysis. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger seas. Supporting previous studies, the Labrador–Irminger seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic Meridional Overturning Circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
Abstract
The basic dynamics of the spatiotemporal diversity for El Niño–Southern Oscillation (ENSO) has been the subject of extensive research and, while several hypotheses have been proposed, remains elusive. One promising line of studies suggests that the observed eastern Pacific (EP) and central Pacific (CP) ENSO may originate from two coexisting leading ENSO modes. We show that the coexistence of unstable EP-like and CP-like modes in these studies arises from contaminated linear stability analysis due to unnoticed numerical scheme caveats. In this two-part study, we further investigate the dynamics of ENSO diversity within a Cane–Zebiak-type model. We first revisit the linear stability issue to demonstrate that only one ENSO-like linear leading mode exists under realistic climate conditions. This single leading ENSO mode can be linked to either a coupled recharge-oscillator (RO) mode favored by the thermocline feedback or a wave-oscillator (WO) mode favored by the zonal advective feedback at the weak air–sea coupling end. Strong competition between the RO and WO modes for their prominence in shaping this ENSO mode into a generalized RO mode makes it sensitive to moderate changes in these two key feedbacks. Modulations of climate conditions yield corresponding modulations in spatial pattern, amplitude, and period associated with this ENSO mode. However, the ENSO behavior undergoing this linear climate condition modulations alone does not seem consistent with the observed ENSO diversity, suggesting the inadequacy of linear dynamics in explaining ENSO diversity. A nonlinear mechanism for ENSO diversity will be proposed and discussed in Part II.
Abstract
The basic dynamics of the spatiotemporal diversity for El Niño–Southern Oscillation (ENSO) has been the subject of extensive research and, while several hypotheses have been proposed, remains elusive. One promising line of studies suggests that the observed eastern Pacific (EP) and central Pacific (CP) ENSO may originate from two coexisting leading ENSO modes. We show that the coexistence of unstable EP-like and CP-like modes in these studies arises from contaminated linear stability analysis due to unnoticed numerical scheme caveats. In this two-part study, we further investigate the dynamics of ENSO diversity within a Cane–Zebiak-type model. We first revisit the linear stability issue to demonstrate that only one ENSO-like linear leading mode exists under realistic climate conditions. This single leading ENSO mode can be linked to either a coupled recharge-oscillator (RO) mode favored by the thermocline feedback or a wave-oscillator (WO) mode favored by the zonal advective feedback at the weak air–sea coupling end. Strong competition between the RO and WO modes for their prominence in shaping this ENSO mode into a generalized RO mode makes it sensitive to moderate changes in these two key feedbacks. Modulations of climate conditions yield corresponding modulations in spatial pattern, amplitude, and period associated with this ENSO mode. However, the ENSO behavior undergoing this linear climate condition modulations alone does not seem consistent with the observed ENSO diversity, suggesting the inadequacy of linear dynamics in explaining ENSO diversity. A nonlinear mechanism for ENSO diversity will be proposed and discussed in Part II.
Abstract
In this study, we investigate how a single leading linear El Niño–Southern Oscillation (ENSO) mode, as studied in Part I, leads to the irregular coexistence of central Pacific (CP) and eastern Pacific (EP) ENSO, a phenomenon known as ENSO spatiotemporal diversity. This diversity is fundamentally generated by deterministic nonlinear pathways to chaos via the period-doubling route and, more prevailingly, the subharmonic resonance route with the presence of a seasonally varying basic state. When residing in the weakly nonlinear regime, the coupled system sustains a weak periodic oscillation with a mixed CP/EP pattern as captured by the linear ENSO mode. With a stronger nonlinearity effect, the ENSO behavior experiences a period-doubling bifurcation. The single ENSO orbit splits into coexisting CP-like and EP-like ENSO orbits. A sequence of period-doubling bifurcation results in an aperiodic oscillation featuring irregular CP and EP ENSO occurrences. The overlapping of subharmonic resonances between ENSO and the seasonal cycle allows this ENSO irregularity and diversity to be more readily excited. In the strongly nonlinear regime, the coupled system is dominated by regular EP ENSO. The deterministic ENSO spatiotemporal diversity is thus confined to a relatively narrow range corresponding to a moderately unstable ENSO mode. Stochastic forcing broadens this range and allows ENSO diversity to occur when the ENSO mode is weakly subcritical. A close relationship among a weakened mean zonal temperature gradient, stronger ENSO activity, and more (fewer) occurrences of EP (CP) ENSO is noted, indicating that ENSO–mean state interaction may yield ENSO regime modulations on the multidecadal time scale.
Abstract
In this study, we investigate how a single leading linear El Niño–Southern Oscillation (ENSO) mode, as studied in Part I, leads to the irregular coexistence of central Pacific (CP) and eastern Pacific (EP) ENSO, a phenomenon known as ENSO spatiotemporal diversity. This diversity is fundamentally generated by deterministic nonlinear pathways to chaos via the period-doubling route and, more prevailingly, the subharmonic resonance route with the presence of a seasonally varying basic state. When residing in the weakly nonlinear regime, the coupled system sustains a weak periodic oscillation with a mixed CP/EP pattern as captured by the linear ENSO mode. With a stronger nonlinearity effect, the ENSO behavior experiences a period-doubling bifurcation. The single ENSO orbit splits into coexisting CP-like and EP-like ENSO orbits. A sequence of period-doubling bifurcation results in an aperiodic oscillation featuring irregular CP and EP ENSO occurrences. The overlapping of subharmonic resonances between ENSO and the seasonal cycle allows this ENSO irregularity and diversity to be more readily excited. In the strongly nonlinear regime, the coupled system is dominated by regular EP ENSO. The deterministic ENSO spatiotemporal diversity is thus confined to a relatively narrow range corresponding to a moderately unstable ENSO mode. Stochastic forcing broadens this range and allows ENSO diversity to occur when the ENSO mode is weakly subcritical. A close relationship among a weakened mean zonal temperature gradient, stronger ENSO activity, and more (fewer) occurrences of EP (CP) ENSO is noted, indicating that ENSO–mean state interaction may yield ENSO regime modulations on the multidecadal time scale.
