The suitability of a linear inverse model (LIM) as a benchmark for decadal surface temperature forecast skill is demonstrated. Constructed from the observed simultaneous and 1-yr lag covariability ...statistics of annually averaged sea surface temperature (SST) and surface (2 m) land temperature global anomalies during 1901–2009, the LIM has hindcast skill for leads of 2–5 yr and 6–9 yr comparable to and sometimes even better than skill of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) model hindcasts initialized annually over the period 1960–2000 and has skill far better than damped persistence (e.g., a local univariate AR1 process). Over the entire post-1901 record, the LIM skill pattern is similar but has reduced amplitude. Pronounced similarity in geographical variations of skill between LIM and CMIP5 hindcasts suggests similarity in their sources of skill as well, supporting additional evaluation of LIM predictability. For forecast leads above 1–2 yr, LIM skill almost entirely results from three nonorthogonal patterns: one corresponding to the secular trend and two more, each with about 10-yr decorrelation time scales but no trend, that represent most of the predictable portions of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) indices, respectively. As found in previous studies, the AMO-related pattern also contributes to multidecadal variations in global mean temperature, and the PDO-related pattern has maximum amplitude in the west Pacific and represents the residual after both interannual and decadal ENSO variability are removed from the PDO time series. These results suggest that current coupled model decadal forecasts may not yet have much skill beyond that captured by multivariate, predictably linear dynamics.
The predictability of seasonal anomalies worldwide rests largely on the predictability of tropical sea surface temperature (SST) anomalies. Tropical forecast skill is also a key metric of climate ...models. We find, however, that despite extensive model development, the tropical SST forecast skill of the operational North American Multi‐Model Ensemble (NMME) of eight coupled atmosphere‐ocean models remains close both regionally and temporally to that of a vastly simpler linear inverse model (LIM) derived from observed covariances of SST, sea surface height, and wind fields. The LIM clearly captures the essence of the predictable SST dynamics. The NMME and LIM skills also closely track and are only slightly lower than the potential skill estimated using the LIM's forecast signal‐to‐noise ratios. This suggests that the scope for further skill improvement is small in most regions, except in the western equatorial Pacific where the NMME skill is currently much lower than the LIM skill.
Key Points
Seasonal tropical SST forecast skill of operational National Multi‐Model Ensemble (NMME) is close to that of simpler linear inverse model
Since operational SST forecast skill is only slightly lower than the estimated potential skill, we may be near the predictability limit
In the western Pacific, improvement of operational ensemble is possible since its skill is much lower than the linear inverse model skill
A multivariate empirical model is used to show that predictability of the dominant patterns of tropical and North Pacific oceanic variability, El Niño–Southern Oscillation (ENSO), and the Pacific ...decadal oscillation (PDO), is mostly limited to little more than a year, despite the presence of spectral peaks on decadal time scales. The model used is a linear inverse model (LIM) derived from the observed simultaneous and 1-yr lag correlation statistics of July–June-averaged SST from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) dataset for the years 1900–2002. The model accurately reproduces the power spectra of the data, including interannual and interdecadal spectral peaks that are significant relative to univariate red noise. Eigenanalysis of the linear dynamical operator yields propagating eigenmodes that correspond to these peaks but have very short decay times and, thus, limited predictability.
Longer-term predictability does exist, however, due to two stationary eigenmodes that are more weakly damped. These eigenmodes do not strongly correspond to the canonical ENSO and PDO patterns. Instead, one is similar to the 1900–2002 trend and might represent anthropogenic effects, while the second represents multidecadal fluctuations of a pattern that potentially represents natural decadal variability; however, neither attribution can be made unambiguously with the analysis presented in this paper. Predictability of these two stationary eigenmodes is significantly enhanced by tropical–North Pacific coupling. Neither stationary eigenmode is well captured in the control run of any coupled GCM in the CMIP-3 project of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), perhaps because in all of the GCMs tropical SST decadal variability is too weak and North Pacific SSTs are too independent of the Tropics.
A key implication of this analysis is that the PDO may represent not a single physical mode but rather the sum of several phenomena, each of which represents a different red noise with its own autocorrelation time scale and spatial pattern. The sum of these red noises can give rise to apparent PDO “regime shifts” and seeming characteristics of a long memory process. Such shifts are not predictable beyond the time scale of the most rapidly decorrelating noise, less than two years, although the expected duration of regimes may be determined from the relative amplitudes of different eigenmodes.
A Linear Inverse Model (LIM) is constructed to evaluate predictability of seasonal sea surface temperature (SST) and sea surface height (SSH) anomalies over the ice‐free global ocean. Its ...ensemble‐mean hindcast skill is also compared to that of the North American Multi‐Model Ensemble (NMME) for 1982–2010. Both have similar skill for dominant modes of SST variability, but regional NMME SST skill is somewhat higher in many locations. However, the LIM has considerably more Atlantic and Southern Ocean SSH skill. Skill is generally comparable along the North American coastline, but LIM skill is greater for several highly productive coastal zones and East Coast tide gauge stations. Diverse, often predictable ENSO events drive teleconnections providing predictability in the North Pacific and along the US West Coast. Predictability in the Atlantic and along the US East Coast is associated with Gulf Stream strength modulation. Overall, the LIM shows potential for seasonal prediction of coastal ocean conditions.
Plain Language Summary
Marine resource management requires a skillful forecast of coastal ocean conditions. Here, we developed and used an empirical prediction system to benchmark the operational system's seasonal prediction skill and understand the source(s) of predictability over the North American Coast. Both systems' skills are generally comparable, but the empirical system's skill is greater for several highly productive coastal zones and East Coast tide gauge stations. The results indicate that the remote impacts of ENSO provide predictability along the US West Coast. Predictability along the US East Coast is associated with Gulf Stream modulation. Overall, our empirical system shows potential for seasonal prediction of coastal ocean conditions.
Key Points
A Linear Inverse Model (LIM) is constructed that can predict monthly mean global sea surface temperature (SST) and sea surface height (SSH) anomalies
The LIM outperforms operational numerical models for SSH skill in the Atlantic and US East Coast tide gauge stations
LIM coastal skill is largely due to capturing ENSO teleconnections impacting the West Coast, and Gulf Stream modulation for the East Coast
The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal ...processes has not been well understood particularly on decadal and longer time scales, and the external influence from the tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a linear inverse model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal time scales. On the other hand, the Indian Ocean dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal time scale. The third mode is partly associated with the subtropical Indian Ocean dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian–Pacific Ocean interaction is a two-way process.
The Pacific Decadal Oscillation, Revisited Newman, Matthew; Alexander, Michael A.; Ault, Toby R. ...
Journal of climate,
06/2016, Letnik:
29, Številka:
12
Journal Article
Recenzirano
The Pacific decadal oscillation (PDO), the dominant year-round pattern of monthly North Pacific sea surface temperature (SST) variability, is an important target of ongoing research within ...themeteorological and climate dynamics communities and is central to the work of many geologists, ecologists, natural resource managers, and social scientists. Research over the last 15 years has led to an emerging consensus: the PDO is not a single phenomenon, but is instead the result of a combination of different physical processes, including both remote tropical forcing and local North Pacific atmosphere–ocean interactions, which operate on different time scales to drive similar PDO-like SST anomaly patterns. How these processes combine to generate the observed PDO evolution, including apparent regime shifts, is shown using simple autoregressive models of increasing spatial complexity. Simulations of recent climate in coupled GCMs are able to capture many aspects of the PDO, but do so based on a balance of processes often more independent of the tropics than is observed. Finally, it is suggested that the assessment of PDO-related regional climate impacts, reconstruction of PDO-related variability into the past with proxy records, and diagnosis of Pacific variability within coupled GCMs should all account for the effects of these different processes, which only partly represent the direct forcing of the atmosphere by North Pacific Ocean SSTs.
Purpose
Hairy cell leukemia (HCL) is a rare mature B cell leukemia. Purine analogs are the mainstay of treatment of HCL, but relapse after purine analog therapy is common. Outcomes of treatment of ...relapsed/refractory HCL typically diminish with each successive line of therapy. Moxetumomab pasudotox-tdfk is a novel recombinant immunotoxin approved for the treatment of patients with relapsed/refractory HCL who have received at least two prior therapies, including a purine analog. This article reviews HCL treatment, focusing on moxetumomab pasudotox-tdfk, its place in therapy, considerations for preparation and administration, and strategies for prevention and management of toxicities.
Methods
A literature search was conducted in the PubMed database from inception to January 2019, using the following terms: moxetumomab, hairy cell leukemia, relapsed/refractory hairy cell leukemia, immunotoxin, and CD22. The package insert and available posters and abstracts were also reviewed.
Results
FDA approval of moxetumomab pasudotox-tdfk was based on a phase III single-arm, open-label trial in 80 patients. Treatment with moxetumomab pasudotox-tdfk yielded a durable complete response rate of 30% with a median duration of response that had not yet been reached at a median follow-up of 16.7 months. The objective response rate was 75% based on blinded independent central review. The most common adverse reactions were infusion-related reactions, edema, nausea, fatigue, headache, pyrexia and anemia. Serious adverse events include capillary leak syndrome and hemolytic uremic syndrome.
Conclusions
Clinicians providing care for patients receiving moxetumomab pasudotox-tdfk should be aware of the strategies required for safe administration, including the management of serious adverse events.
ENSO's atmospheric teleconnections drive anomalous North Pacific sea surface temperatures through changes in surface heat fluxes (“the atmospheric bridge”). Previous research focusing on the bridge ...as a seasonal phenomenon did not consider how ENSO‐related changes in synoptic variability might also impact surface turbulent heat fluxes (STHF). In this study, we find that while well over half of ENSO's impact on STHF occurs on low‐frequency (>8 days) time scales, up to 20% of its impact arises on high‐frequency (<8 days) time scales, through changes in the covariance between surface wind speed and air‐sea enthalpy difference that typically warms the ocean south of the storm track. During El Niño, the North Pacific storm track and its attendant sea surface warming shift southward, reducing warming of the central North Pacific ocean and thereby enhancing the bridge signal there. Additionally, changes in the bulk formula coefficients between ENSO phases drive STHF differences (5%–10%).
Plain Language Summary
The El Niño‐Southern Oscillation (ENSO) has global impacts through teleconnections, which can influence the underlying ocean via the “atmospheric bridge.” The atmospheric bridge results in upward surface heat flux and colder sea surface temperature (SST) over the central North Pacific and downward surface heat flux and warmer SST along the coast of North America during El Niño winters. While previous research has studied the influence of longer timescale features on this bridge‐related surface heat flux pattern, the corresponding impact of shorter timescale variability remains unexplored. We find that while longer timescales (>8 days) dominate, shorter timescale influence on the fluxes is non‐negligible. Shorter timescale variability warms the ocean south of the track of North Pacific storms. This warming contribution shifts with the storm location between the two ENSO phases. While the resulting difference between ENSO phases is small, it is mostly aligned with the total surface turbulent heat flux (STHF) difference between El Niño and La Niña. Also, the bulk formula coefficients used to compute the fluxes vary, leading to 5%–10% of the STHF difference between ENSO phases. In summary, the contribution of shorter timescale variability and coefficient changes to the North Pacific bridge‐related STHF is small but non‐negligible.
Key Points
While seasonal variability dominates ENSO‐related surface turbulent heat fluxes over the North Pacific, synoptic effects are non‐negligible
ENSO‐related storm track shift affects the high‐frequency contribution to the turbulent heat fluxes
Differences in the bulk formula coefficient between two ENSO phases have a small but non‐negligible influence on the surface heat fluxes
Abstract
Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority ...for operational forecasting centers, because of the NAO’s association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.
Marine heatwaves (MHWs)-extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences-have increased worldwide in recent decades. ...Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can-on its own-appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.
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