This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific–North American pattern (PNA), El Niño–Southern Oscillation (ENSO), ...Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climatemodes. Substantial variability in skill scoresmanifests across realizations fromindividual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM–PNA first-order interaction and underestimate the NAM–AMO connection. These results suggest that the choice of ESMand ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term.
SIGNIFICANCE STATEMENT: Internal climate variability occurs over multiple spatial and temporal scales and is encapsulated in a series of internal climate modes. The representation of such modes in climate models is a critically important aspect of model fidelity. Analyses presented herein uses several skill scores to evaluate both the spatial and temporal manifestations of these climate modes in the CMIP6 generation of Earth system models (ESMs). There is marked variability in model fidelity for these modes and this variability in credibility within the current climate has important implications for the choice of specific ESMs and ESM realizations in making climate projections.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models including those participating in ...phase 6 of the Coupled Model Intercomparison Project (CMIP) compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) and the closely related North Atlantic Oscillation (NAO), and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Variations of global wind are important in changing the atmospheric structure and circulation, in coupling of atmospheric layers, and in influencing
the wave propagations. Due to the difficulty of ...directly measuring zonal
wind from the stratosphere to the lower thermosphere, we derived a global
balance wind (BU) dataset from 50∘ S to 50∘ N and during
2002–2019 using the gradient wind theory and SABER temperatures and
modified by meteor radar observations at the Equator. The dataset captures
the main feature of global monthly mean zonal wind and can be used to study
the variations (i.e., annual, semi-annual, ter-annual, and linear) of zonal
wind and the responses of zonal wind to quasi-biennial oscillation (QBO),
El Niño–Southern Oscillation (ENSO), and solar activity (F10.7). The same procedure is performed on the MERRA-2 zonal wind (MerU) to validate BU and its responses below 70 km. The annual, semi-annual, and ter-annual oscillations of
BU and MerU have similar amplitudes and phases. The semi-annual oscillation
of BU has peaks around 80 km, which are stronger in the southern tropical
region and coincide with previous satellite observations. As the increasing
of the values representing QBO wind, both values of
representing BU and MerU (short for BU and MerU) change from increasing to
decreasing with the increasing height and extend from the Equator to higher
latitudes. Both BU and MerU increase with the increasing of the values of
multivariate ENSO index (MEI) and decrease with increasing F10.7 in the southern stratospheric polar jet region below 70 km. The responses of winds to ENSO and F10.7 exhibit
hemispheric asymmetry and are more significant in the southern polar jet
region. While above 70 km, BU increases with the increasing of MEI and
F10.7. The negative linear changes of BU at 50∘ N are absent in
MerU during October–January. The discussions on the possible influences of
the temporal intervals and sudden stratospheric warmings (SSWs) on the
variations and responses of BU illustrate the following: (1) the seasonal variations
and the responses to QBO are almost independent on the temporal intervals
selected; (2) the responses to ENSO and F10.7 are robust but slightly depend on the temporal intervals; (3) the linear changes of both BU and MerU depend strongly on the temporal intervals; (4) SSWs affect the magnitudes but do
not affect the hemispheric asymmetry of the variations and responses of BU
at least in the monthly mean sense. The variations and responses of global
zonal wind to various factors are based on BU, which is derived from
observations, and thus provide a good complement to model studies and
ground-based observations.
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in ...atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.
The study of the interannual and intraseasonal modulation of tropical cyclogenesis (TCG) in the northwest Pacific (NWP) basin has received significant attention in the past. Most previous studies ...have focused on the individual impact of El Niño‐southern oscillation (ENSO) or Madden–Julian Oscillation on TCG, whereas the combined effect of ENSO and another leading intraseasonal oscillation mode, quasi‐biweekly oscillation (QBWO), on NWP TCG has rarely been studied. Therefore, this study systemically investigates the modulation of NWP TCG by the QBWO under different ENSO phases. A significant modulation of TCG by QBWO in the ENSO neutral years is observed, with significantly increased (decreased) TCG over the NWP in QBWO active (inactive) phases. Moreover, during El Niño years and La Niña years, the modulation of TCG by QBWO is not significant and no distinctive difference in the TCG frequency can be detected. Further analyses show that the modulation of TCG by the QBWO is mainly related to the changes in large‐scale environment and energy that affect TCG associated with active or inactive phases of the QBWO under different ENSO phases. Large‐scale environmental factors and energy are more favourable for TCG during ENSO neutral years compared with El Niño and La Niña years. These results are associated with QBWO cycle under the impact of background (e.g., boundary layer moisture and zonal wind shear) which is determined by ENSO conditions.
A 10–20‐day filtered outgoing longwave radiation anomalies in quasi‐biweekly oscillation active and inactive phases in climatology (a, b), neutral years (c, d), El Niño years (e, f) and La Niña years (g, h) (unit: W/m2). The black dots indicate the locations of tropical cyclogenesis which is calculated from Japan Meteorological Agency dataset.
The Madden–Julian Oscillation (MJO) is the leading mode of sub‐seasonal variability in the tropical atmosphere and is a source of predictability for extratropical weather through its teleconnections. ...MJO teleconnection patterns can be modulated by the El Niño–Southern Oscillation (ENSO) on seasonal to interannual time scales. However, changes over decadal time scales are less well understood. ERA5 reanalysis data are used to show that the boreal winter MJO teleconnection pattern in the Northern Hemisphere has changed in recent decades in line with changes in the Pacific Decadal Oscillation and Atlantic Multidecadal Variability. Changes are seen in the circulation, temperature and precipitation responses. In particular, from 1997, intraseasonal cold anomalies appear over Europe and the eastern United States due to MJO convection over the western Pacific; these were not present 20 years previously. The decadal variability observed is not the product of aliasing of ENSO modulation of the teleconnection.
Plain Language Summary
Weather in different regions of the globe can be linked by planetary‐scale atmospheric waves, and these links can help forecasters to predict the weather. One such link, or teleconnection pattern, connects changes in rainfall over Indonesia and the tropical Pacific (from a weather system called the Madden–Julian Oscillation or MJO) to changes in the weather in North America and Europe. This study assesses this teleconnection pattern in two separate time periods (roughly the mid‐1970s to mid‐1990s and mid‐1990s to late 2010s) to analyze if and how it has changed. We find that the pattern has changed, and that this is due to large‐scale changes in the background state of the atmosphere. These changes in the link between the tropics and extratropics will have implications for weather forecasts on weekly to monthly time scales.
Key Points
The extratropical response to the Madden‐Julian Oscillation has changed on decadal time scales
This decadal variability coincides with changes in low‐frequency oceanic modes in both the Pacific and Atlantic basins
Changes on decadal time scales are different to those modulated by the El Niño‐Southern Oscillation on interannual scales
We present a general Bayesian hierarchical framework for conducting nonstationary frequency analysis of multiple hydrologic variables. In this, annual maxima from each variable are assumed to follow ...a generalized extreme value (GEV) distribution in which the location parameter is allowed to vary in time. A Gaussian elliptical copula is used to model the joint distribution of all variables. We demonstrate the utility of this framework with a joint frequency analysis model of annual peak snow water equivalent (SWE), annual peak flow, and annual peak reservoir elevation at Taylor Park dam in Colorado, USA. Indices of large‐scale climate drivers—El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are used as covariates to model temporal nonstationarity. The Bayesian framework provides the posterior distribution of the model parameters and consequently the return levels. Results show that performing a multivariate joint frequency analysis reduces the uncertainty in return level estimates and better captures multivariate dependence compared to an independent model.
Plain Language Summary
In this study, we develop a method for determining the probability of occurrence of rare hydrologic events (e.g., floods). Utilizing modern statistical methods, we are able to estimate occurrence probabilities for multiple hydrologic variables simultaneously while incorporating climate information that changes in time. We apply this technique to estimate occurrence probabilities for streamflow, reservoir elevation, and snow levels for the Taylor Park reservoir in Colorado, USA. This method provides several benefits over traditional methods including reduction of uncertainty and a flexible model structure which allows for the incorporation of climate information.
Key Points
A model for nonstationary multivariate hydrologic frequency analysis is developed
The model allows for the incorporation of climate covariates and the specification of a nonlinear dependence between variables
Multivariate frequency analysis capture the dependence between multiple hydrologic variables
Interannual variation of seasonal-mean tropical convection over the Indo-Pacific region is primarily controlled by El Niño–Southern Oscillation (ENSO). For example, during El Niño winters, ...seasonal-mean convection around the Maritime Continent becomes weaker than normal, while that over the central to eastern Pacific is strengthened. Similarly, subseasonal convective activity, which is associated with the Madden–Julian oscillation (MJO), is influenced by ENSO. The MJO activity tends to extend farther eastward to the date line during El Niño winters and contract toward the western Pacific during La Niña winters. However, the overall level of MJO activity across the Maritime Continent does not change much in response to the ENSO. It is shown that the boreal winter MJO amplitude is closely linked with the stratospheric quasi-biennial oscillation (QBO) rather than with ENSO. The MJO activity around the Maritime Continent becomes stronger and more organized during the easterly QBO winters. The QBO-related MJO change explains up to 40% of interannual variation of the boreal winter MJO amplitude. This result suggests that variability of the MJO and the related tropical–extratropical teleconnections can be better understood and predicted by taking not only the tropospheric circulation but also the stratospheric mean state into account. The seasonality of the QBO–MJO link and the possible mechanism are also discussed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model ...performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden–Julian oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to bemore skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3–4 and phase 7 have better NAO forecast skills compared with the other phases. The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “windows of opportunity” for subseasonal to seasonal predictions.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Volcanic eruptions can have significant climate impacts and serve as useful natural experiments for better understanding the effects of abrupt, externally forced climate change. Here, we investigate ...the Indian Ocean Dipole's (IOD) response to the largest tropical volcanic eruptions of the last millennium. Post‐eruption composites show a strong negative IOD developing in the eruption year, and a positive IOD the following year. The IOD and El Niño‐Southern Oscillation (ENSO) show a long‐term damped oscillatory response that can take up to 8 years to return to pre‐eruptive baselines. Moreover, the Interdecadal Pacific Oscillation (IPO) phase at the time of eruption controls the IOD response to intense eruptions, with negative (positive) IPO phasing favoring more negative (positive) IOD values via modulation of the background state of the eastern Indian Ocean thermocline depth. These results have important implications for climate risk in low‐likelihood, high‐impact scenarios, particularly in vulnerable communities unprepared for IOD and ENSO extremes.
Plain Language Summary
Volcanic eruptions were previously found to affect the El Niño‐Southern Oscillation in the Pacific, but little is known about their effect on the Indian Ocean. In this study, we use simulations of the strongest tropical eruptions of the last millennium to better understand the effects of volcanic activity on the Indian Ocean. We find a consistent response of sea surface temperatures and surface winds across the basin between different simulated eruptions. This response scales with eruption intensity and is long‐lived, lasting for multiple years. We also find that sea surface temperatures in the tropical Pacific at the time of eruption influence the strength of the initial tropical Indian Ocean response. Understanding these connections will allow for improved climate predictability and preparedness following large tropical eruptions.
Key Points
Both Indian Ocean Dipole (IOD) and El Niño‐Southern Oscillation (ENSO) show oscillatory responses for up to 8 years following strong tropical volcanic eruptions
These eruptions induce a negative IOD followed by a positive IOD, and this response scales with eruption intensity
Initial Interdecadal Pacific Oscillation phasing at time of eruption influences the post‐eruption response via low‐frequency thermocline depth modulation