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This paper describes a framework for identifying dominant atmospheric drivers of ocean variability. The method combines statistics of atmosphere–ocean fluxes with physics from an ocean general circulation model to derive atmospheric patterns optimized to excite variability in a specified ocean quantity of interest. We first derive the method as a weighted principal components analysis and illustrate its capabilities in a toy problem. Next, we apply our analysis to the problem of interannual upper ocean heat content (HC) variability in the North Atlantic Subpolar Gyre (SPG) using the adjoint of the MITgcm and atmosphere–ocean fluxes from the ECCOv4-r4 state estimate. An unweighted principal components analysis reveals that North Atlantic heat and momentum fluxes in ECCOv4-r4 have a range of spatiotemporal patterns. By contrast, dynamics-weighted principal components analysis collapses the space of these patterns onto a small subset—principally associated with the North Atlantic Oscillation—that dominates interannual SPG HC variance. By perturbing the ECCOv4-r4 state estimate, we illustrate the pathways along which variability propagates from the atmosphere to the ocean in a nonlinear ocean model. This technique is applicable across a range of problems across Earth system components, including in the absence of a model adjoint. Significance Statement: While the oceans have absorbed 90% of the excess heat associated with human-forced climate change, the change in the ocean's heat content is not steady, with peaks and troughs superimposed upon a general increase. These fluctuations come from chaotic changes in the atmosphere and ocean and can be hard to disentangle. We use this case of ocean heat content variability to introduce a new method for determining the patterns of weather and climate in the atmosphere that are most effective at generating fluctuations in the ocean. To do this, we combine the statistics of recent atmospheric activity with output from a state-of-the-art numerical ocean model that reveals physical processes driving changes in ocean quantities including ocean heat content. This approach suggests that the atmospheric patterns that stimulate the most energetic changes in ocean heat content in the northern North Atlantic are not necessarily the most energetic patterns present in the atmosphere. We test our findings by preventing these patterns from affecting the ocean in our numerical model and measure a strong reduction in ocean heat content fluctuations. [ABSTRACT FROM AUTHOR]

Dimension reduction techniques are an essential part of the climate analyst's toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative, sparse, and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a widely used technique to identify modes of low-frequency variability of atmospheric circulation and their spatial changes. However, it turns out that PCA is highly sensitive to the period analyzed and the length of the time window used. Its results can vary considerably if the period is shifted by even 1 year. We present temporal variability of modes from the late nineteenth century using moving PCA of winter (DJF) monthly mean 500-hPa height anomalies for 20–50-yr moving periods with 1-yr step. We employ the congruence coefficient to compare spatial patterns of the modes and identify their substantial changes. Shorter moving periods are more susceptible to sudden fluctuations in mode patterns from one period to the next, while longer periods yield more stable results. We strongly recommend applying a moving PCA to detect spatial changes in modes of low-frequency variability, as it unveils any hidden sudden changes in the modes. These changes can be influenced by many aspects, such as data quality, sampling variability, and length of the analyzed period. Spatial patterns of the Atlantic–European modes are more stable across ensemble members than those over the Pacific and North America, especially before the 1920s. During this period, North Atlantic and European modes explain more variance in the ensemble mean than in ensemble members, while the reverse holds for Pacific and North American modes. In data-sparse regions, modes in ensemble members exhibit greater variability. The process of averaging then leads to weaker modes in the ensemble mean, explaining less variance compared to ensemble members. [ABSTRACT FROM AUTHOR]

AbstractSouthern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO2 to the ocean interior. The relationships between mixed-layer ventilation, the Southern Annular Mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means.In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index (r = 0.82) and reflect SAM’s role in driving cyclone intensity and, in turn, extreme westerly winds. This Joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed-layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to mid-latitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode.The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs.

Land-use and land-cover changes (hereafter simply "land use") alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (tLU) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that tLU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal variability of drought over the entire Southeast Asia (SEA) region and its associations with the large-scale climate drivers during the period 1960–2019 are investigated for the first time. The 12-month Standardized Precipitation Evapotranspiration Index (SPEI) was computed based on the monthly Global Precipitation Climatology Centre (GPCC) precipitation and the monthly Climate Research Unit (CRU) 2-m temperature. The relationships between drought and large-scale climate drivers were examined using the principal component analysis (PCA) and maximum covariance analysis (MCA) techniques. Results showed that the spatiotemporal variability of drought characteristics over SEA is significantly different between mainland Indochina and the Maritime Continent and the difference has been increased substantially in recent decades. Moreover, the entire SEA is divided into four homogeneous drought subregions. Drought over SEA is strongly associated with oceanic and atmospheric large-scale drivers, particularly El Nino˜ –Southern Oscillation (ENSO), following by other remote factors such as the variability of sea surface temperature (SST) over the tropical Atlantic, the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IOD). In addition, there exists an SST anomaly dipole over the Pacific Ocean, which modulates the atmospheric circulations and consequently precipitation over SEA, affecting drought conditions in the study region. [ABSTRACT FROM AUTHOR]

A proper description of ocean–atmosphere interactions is key for a correct understanding of climate evolution. The interplay among the different variables acting over the climate is complex, often leading to correlations across long spatial distances (teleconnections). On some occasions, those teleconnections occur with quite significant temporal shifts that are fundamental for the understanding of the underlying phenomena but that are poorly captured by standard methods. Applying orthogonal decomposition such as maximum covariance analysis (MCA) to geophysical datasets allows us to extract common dominant patterns between two different variables, but generally suffers from (i) the nonphysical orthogonal constraint as well as (ii) the consideration of simple correlations, whereby temporally offset signals are not detected. Here we propose an extension, complex rotated MCA, to address both limitations. We transform our signals using the Hilbert transform and perform the orthogonal decomposition in complex space, allowing us to correctly correlate out-of-phase signals. Subsequent varimax rotation removes the orthogonal constraints, leading to more physically meaningful modes of geophysical variability. As an example, we have employed this method on sea surface temperature and continental precipitation; our method successfully captures the temporal and spatial interactions between these two variables for (i) the seasonal cycle, (ii) canonical ENSO, (iii) the global warming trend, (iv) the Pacific decadal oscillation, (v) ENSO Modoki, and finally (vi) the Atlantic meridional mode. The complex rotated modes of MCA provide information on the regional amplitude and, under certain conditions, the regional time lag between changes on ocean temperature and land precipitation. Significance Statement: Correlations between time series of different climate variables are often time-lagged and can appear over long spatial distances. Our goal was to develop a method that allows the simultaneous identification of the dominant spatial patterns and their time lags between two different climate variables. Using sea surface temperatures and continental rainfalls as an example, our method extracts the different dynamics of the seasonal cycle as well as five other well-known climate phenomena. Especially for cyclic time series like the seasonal cycle, the relative time lags at different locations can be determined precisely, whereas for acyclic time series only qualitative statements about time lags can be made. In future studies, we expect new insights into the dynamical structure of the Madden–Julian oscillation thanks to this method, which is readily available as a Python package. [ABSTRACT FROM AUTHOR]

In this paper we develop a method to quantify the accuracy of different pattern extraction techniques for the additive space–time modes often assumed to be present in climate data. It has previously been shown that the standard technique of principal component analysis (PCA; also known as empirical orthogonal functions) may extract patterns that are not physically meaningful. Here we analyze two modern pattern extraction methods, namely dynamical mode decomposition (DMD) and slow feature analysis (SFA), in comparison with PCA. We develop a Monte Carlo method to generate synthetic additive modes that mimic the properties of climate modes described in the literature. The datasets composed of these generated modes do not satisfy the assumptions of any pattern extraction method presented. We find that both alternative methods significantly outperform PCA in extracting local and global modes in the synthetic data. These techniques had a higher mean accuracy across modes in 60 out of 60 mixed synthetic climates, with SFA slightly outperforming DMD. We show that in the majority of simple cases PCA extracts modes that are not significantly better than a random guess. Finally, when applied to real climate data these alternative techniques extract a more coherent and less noisy global warming signal, as well as an El Niño signal with a clearer spectral peak in the time series, and more a physically plausible spatial pattern. [ABSTRACT FROM AUTHOR]

Atmosphere and ocean are coupled via air–sea interactions. The atmospheric conditions fuel the ocean circulation and its variability, but the extent to which ocean processes can affect the atmosphere at decadal time scales remains unclear. In particular, such low-frequency variability is difficult to extract from the short observational record, meaning that climate models are the primary tools deployed to resolve this question. Here, we assess how the ocean's intrinsic variability leads to patterns of upper-ocean heat content that vary at decadal time scales. These patterns have the potential to feed back on the atmosphere and thereby affect climate modes of variability, such as El Niño or the interdecadal Pacific oscillation. We use the output from a global ocean–sea ice circulation model at three different horizontal resolutions, each driven by the same atmospheric reanalysis. To disentangle the variability of the ocean's direct response to atmospheric forcing from the variability due to intrinsic ocean dynamics, we compare model runs driven with interannually varying forcing (1958–2018) and model runs driven with repeat-year forcing. Models with coarse resolution that rely on eddy parameterizations show (i) significantly reduced variance of the upper-ocean heat content at decadal time scales and (ii) differences in the spatial patterns of low-frequency variability compared with higher-resolution models. Climate projections are typically done with general circulation models with coarse-resolution ocean components. Therefore, these biases affect our ability to predict decadal climate modes of variability and, in turn, hinder climate projections. Our results suggest that for improving climate projections, the community should move toward coupled climate models with higher oceanic resolution. [ABSTRACT FROM AUTHOR]

Southern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO2 to the ocean interior. The relationships between mixed layer ventilation, the southern annular mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means. In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index (r = 0.82) and reflect the SAM's role in driving cyclone intensity and, in turn, extreme westerly winds. The joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to midlatitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode. The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs. [ABSTRACT FROM AUTHOR]

The South China coast suffers frequent heavy rainfall every warm season. Based on the objective classification method of principal components analysis, the key role of the synoptic pattern in determining the heavy rainfall processes that occurred over the South China coast in the warm season during 2008–18 is examined in this study. We found that heavy rainfall occurs most frequently under three typical synoptic patterns (P1–P3 hereafter) characterized by strong low-level onshore winds. P1 and P3 feature a prevailing southwesterly monsoonal flow in the lower troposphere, with heavy rainfall frequently occurring over the inland windward region in the afternoon associated with the orographic lifting and solar heating. The onshore wind of P3 is stronger than P1 as the western Pacific subtropical high extends more westward to 122°E, which induces stronger low-level convergence along the coastline than P1 when the ageostrophic wind veers from the offshore to onshore direction in the early morning. Hence, a secondary early morning rainfall peak can be found along the coastline. P2 is characterized by a low-level vortex located over the southwest portion of south China. Heavy rainfall under P2 usually initiates over the western part of the coastal region in the morning and then propagates inland in the afternoon. Overall, the synoptic patterns strongly determine the spatial distribution and diurnal cycle of heavy rainfall over the South China coast. This heavy rainfall is closely related to the diurnally varying low-level onshore winds rather than the low-level jets, as well as the different interactions between the low-level onshore winds and the local orography, coastline, and land–sea breeze circulations under different synoptic patterns. [ABSTRACT FROM AUTHOR]

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3--60-day periods) in both GPH and SST and El Niño--Southern Oscillation (ENSO) at low frequencies (2--7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics. [ABSTRACT FROM AUTHOR]

Understanding the Tibetan Plateau (TP) thermal processes is of utmost significance in changing climate. This study investigates the effect of soil moisture in changing the TP thermal profile and consequently summer precipitation in South Asia (SA). Soil moisture from Special Sensor Microwave Imager (SSM/I) developed from the F-08, F-11, and F-13 fundamental climate data record and atmospheric reanalysis from ERA-Interim, MERRA-2, and NCEP/CFSR during 1988–2008 are used. A generalized linear method that assesses the reciprocal forcing between two connected fields, named the coupled manifold technique (CMT), is applied to TP soil moisture and SA summer precipitation. It is revealed that interannual variations of SA precipitation are significantly (confidence level = 99%) impacted by TP soil moisture and the explained ratio of variance in SA is 0.3–0.4. Composite analysis indicates that SA summer precipitation has positive anomalies in response to dry TP soil moisture in the previous spring and vice versa. For understanding the possible mechanism, thermal processes, relative humidity, wind components, and moisture flux anomalies were calculated for dry and wet TP soil moisture and summer precipitation. The results suggested that TP soil moisture is likely to regulate near-surface energy balance and diabatic heating profile over TP. As a result, the surrounding lower-level westerlies (easterlies) (at 850 hPa) converge (diverge), associated with divergence (convergence) at the upper troposphere (200 hPa). The westerlies (easterlies) are usually moisture-rich (moisture-deficient) and thus cause more (less) precipitation in western (eastern) SA. It is thus suggested that the spring soil moisture may affect the thermal profile of TP, affecting the summer precipitation in SA as a consequence. [ABSTRACT FROM AUTHOR]

Modes of low-frequency circulation variability in the Northern Hemisphere extratropics are compared between five reanalyses. Circulation modes are detected by rotated principal component analysis (PCA) of monthly mean 500-hPa geopotential heights between 1957 and 2002, separately for individual seasons. The quantification of differences between reanalyses is based on the percentage of grid points (approximately corresponding to the percentage of area) where the spatial representations of a mode (loadings) significantly differ between reanalyses. The differences between surface-input reanalyses (20CRv2c, ERA-20C) and full-input reanalyses (NCEP-1, ERA-40, JRA-55) are larger than differences within the reanalysis groups in all seasons except for autumn. The causes of the differences are of two kinds. First, the differences may be inherent to PCA: namely, the spatial structure of the modes may be sensitive to the number of components rotated. This concerns only a few modes. Second, the differences may reflect real correlation structures in reanalysis data. We demonstrate that the differences concentrate in three or fewer modes in each season. The reanalysis most different from the rest is 20CRv2c, with the differences concentrating over the southern half of Asia and in the subtropical belt over the Pacific and adjacent southwestern North America. The 20CRv2c reanalysis disagrees from other reanalyses there predominantly before the 1980s, which points to the impact of insufficient amount of assimilated observations. On the contrary, ERA-20C exhibits a higher agreement with full-input reanalyses, which is why we recommend it for studies of atmospheric circulation over the entire twentieth century. [ABSTRACT FROM AUTHOR]

We propose a method for analyzing extremal behavior through the lens of a most efficient basis of vectors. The method is analogous to principal component analysis, but is based on methods from extreme value analysis. Specifically, rather than decomposing a covariance or correlation matrix, we obtain our basis vectors by performing an eigendecomposition of a matrix that describes pairwise extremal dependence. We apply the method to precipitation observations over the contiguous United States. We find that the time series of large coefficients associated with the leading eigenvector shows very strong evidence of a positive trend, and there is evidence that large coefficients of other eigenvectors have relationships with El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

The variability of the surface zonal and meridional wind components over northeastern North America during June–October is analyzed through a statistical downscaling (SD) approach that relates the main wind and large-scale circulation modes. An observational surface wind dataset of 525 sites over 1953–2010 provides the local information. Twelve global reanalyses provide the large-scale information. The large-to-local variability of the wind field can be explained, to a large extent, in terms of four coupled modes of circulation explaining a similar amount of variance. The SD method is mostly sensitive to the number of retained modes and subregionally to the large-scale information variable, but not to the reanalysis source. The SD methodological uncertainty based on the use of multiple configurations is directly related to the variability of the wind, similar in relative terms for both components. With an adequate choice of parameters the SD estimates provide more realistic variances than the reanalysis wind, although their correlations with respect to observations are lower than the latter. Additionally, while these different SD estimations are very similar on the reanalysis used, the various reanalysis wind fields show noticeable differences, especially in their variances. The wind variability is reconstructed back to 1850, making use of century-long reanalyses and two additional SLP gridded datasets, which allows estimating the variability at decadal to multidecadal time scales. Recent negative (significant) trends in the zonal component do not stand out in the multidecadal context, but they are consistent with a global stilling process, and are partially attributable to changes in the large-scale dynamics. [ABSTRACT FROM AUTHOR]

This article provides a reconciling perspective on the two main, but contradictory, interpretations of the southern annular mode (SAM). SAM was originally thought to characterize meridional shifts in the storm track across the entire hemisphere. This perspective was later questioned, and SAM was interpreted as a statistical artifact depending on the choice of base region for the principal component analysis. Neither perspective, however, fully describes SAM. We show that SAM cannot be interpreted in terms of midlatitude variability, as SAM merely modulates the most poleward part of the cyclone tracks and only marginally influences the distribution of other weather-related features of the storm track (e.g., position of jet axes and Rossby wave breaking). Instead, SAM emerges as the leading pattern of geopotential variability due to strong correlations of sea level pressure around the Antarctic continent. As SAM correlates strongly both with the pan-Antarctic mean temperature and the meridional heat flux through 65°S, we hypothesize that SAM can be interpreted as a measure of the degree of the (de)coupling between Antarctica and the southern midlatitudes. As an alternative way of characterizing southern midlatitude variability, we seek domains in which the leading EOF patterns of both the geopotential and storm-track features yield a dynamically consistent picture. This approach is successful for the South Pacific. Here the leading variability patterns are closely related to the Pacific–South America pattern and point toward an NAO-like variability. [ABSTRACT FROM AUTHOR]

A statistical learning approach to produce seasonal temperature forecasts in western Europe and Scandinavia was implemented and tested. The leading principal components (PCs) of sea surface temperature (SST) and the geopotential at the 150-hPa level (GPT) were derived from reanalysis datasets and used at different lags (from one to five seasons) as predictors. Random sampling of both the fitting years and the potential predictors together with the Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to create a large ensemble of statistical models. Applying the models to independent test years shows that the ensemble performs well over the target areas and that the ensemble mean is more accurate than the best individual ensemble member on average. Skillful results were especially found for summer and fall, with the anomaly correlation coefficient values ranging between 0.41 and 0.68 for these seasons. The correct simulation of decadal trends, using sufficiently long time series for fitting (70 years), and the use of lagged predictors increased the prediction skill. The decadal-scale variability of SST, most importantly the Atlantic multidecadal oscillation (AMO), and different PCs of GPT are the most important individual predictors among all predictors. Both SST and GPT bring equally much predictive power, although their importance is different in different seasons. [ABSTRACT FROM AUTHOR]

This work aims to isolate and quantify the local and remote biogeophysical influences of slowly varying vegetation variability on the climate of La Plata basin (LPB) in the austral spring season (September–November) using observational records. Past studies have shown strong land–atmosphere coupling in LPB during this season. The analysis uses a 34-yr record (1981–2014) of the modified enhanced vegetation index (EVI2) from the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology dataset and the third-generation normalized difference vegetation index (NDVI) from Global Inventory Modeling and Mapping Studies. The dominant patterns of vegetation index variability in space and time are assessed using empirical orthogonal function/principal component analysis over the LPB. The dominant mode in the austral spring is a vegetation dipole, with greening (browning) or positive (negative) vegetation index anomalies in the northeastern (southwestern) part of the basin. Using the stepwise generalized equilibrium feedback assessment (SGEFA), the effect of the vegetation variability on the atmosphere is then isolated. The dominant mode of LPB vegetation variability in austral spring is related to warmer temperatures in the southwest LPB and enhanced precipitation over the central and southern parts of the basin. A mechanism is proposed for the increase in latent heat flux and cooler temperatures in the northeastern LPB due to greening, and the increase in sensible heat flux, warmer temperatures, and decrease in surface pressure in southwestern LPB due to browning. The geostrophic response to this induced pressure gradient leads to anomalous northerly enhancement of moisture-laden winds, deeper penetration of moisture into LPB, and increased precipitation over the central and southern parts of the basin. [ABSTRACT FROM AUTHOR]

The study develops an approach to assess co-behavior of climate processes. The regional response of precipitation and temperature patterns over southern Africa to the combined roles (co-behavior) of El Niño–Southern Oscillation (ENSO), Antarctic Oscillation (AAO), and intertropical convergence zone (ITCZ) is evaluated. Self-organizing maps (SOMs) classify circulation patterns over the subcontinent, and principal component analysis (PCA) is used to identify related patterns across the data. The tropical rain belt index (TRBI), a measure of the ITCZ, is generally in phase with the AAO but mostly out of phase with ENSO. The phases of AAO may enhance or suppress ENSO impact on the location and distribution of regional precipitation and temperature over the region. This understanding of the co-behavior of large-scale processes is important to assess the impact these processes collectively have on precipitation and temperature, especially under future climate forcings. [ABSTRACT FROM AUTHOR]

Global changes, such as human activities and climate change, increase the odds of worsening drought. The Gravity Recovery and Climate Experiment (GRACE) satellite provides an opportunity to monitor drought levels by the total amount of water, instead of using a small finite set of water cycle elements or indirect indicators. The potential gap lies in the insufficient size of the GRACE record. The database does not meet the requirements of a stationary annual cycle calculated over a relatively long period as recommended by the IPCC, and the disturbance from long-term global changes is often not considered. In this work, a GRACE-based modulated water deficit (GRACE-MWD) process for drought monitoring under the modulated annual cycle (MAC) reference frame in southwest China was proposed. GRACE-MWD achieved a higher ratio of agreement with the standardized precipitation evapotranspiration index at a time scale of 3 months (SPEI03): it ranged from 0.48 to 0.84, while the GRACE-based drought severity index (GRACE-DSI) ranged from 0.48 to 0.68. Compared with remote sensing datasets widely used in drought monitoring, GRACE-MWD data are less affected by seasonality from land-cover categories, which benefit from the MAC reference frame. The ratio-of-agreement metric for the study area showed that GRACE-MWD had a time scale between 7 and 11 months in reference to SPEI and the standardized precipitation index (SPI). The stability of the MAC reference frame to GRACE-MWD was further discussed when GRACE records were extended and was more stable than that of the stationary annual cycle. GRACE-MWD meets global changes via an adaptive reference frame, which is worthy of generalizing to global applications. [ABSTRACT FROM AUTHOR]

This paper presents a method to homogenize China's surface solar radiation (SSR) data and uses the resulting homogenized SSR data to assess the SSR trend over the period 1958-2016. Neighboring surface sunshine duration (SSD) data are used as reference data to assess the SSR data homogeneity. A principal component analysis is applied to build a reference series, which is proven to be less sensitive to occasional data issues than using the arithmetic mean of data from adjacent stations. A relative or absolute test is applied to detect changepoints, depending on whether or not a suitable reference series is available. A quantile-matching method is used to adjust the data to diminish the inhomogeneities. As a result, 60 out of the 119 SSR stations were found to have inhomogeneity issues. These were mainly caused by changes in instrument and observation schedule. The nonclimatic changes exaggerated the SSR change rates in 1991-93 and resulted in a sudden rise in the national average SSR series, causing an unrealistically drastic trend reversal in the 1990s. This was diminished by the data homogenization. The homogenized data show that the national average SSR has been declining significantly over the period 1958-90; this dimming trend mostly diminished over the period 1991-2005 and was replaced by a brightening trend in the recent decade. From the homogenized SSR data, the 1958-90 and 1958-2005 dimming rate is estimated to be -6.13 ± 0.47 and -5.08 ± 0.27W m-2 decade-1, respectively, and the 2005-16 brightening rate is 6.13 ± 1.77W m-2 decade-1. [ABSTRACT FROM AUTHOR]

The influence of atmospheric circulation on winter temperature and precipitation trends over Europe in the period 2006-50 is investigated in a 21-member initial condition ensemble from a fully coupled global climate model and in a multimodel framework consisting of 40 different models. Five versions of a dynamical adjustment method based on empirical orthogonal function analysis of sea level pressure are introduced, and their performance in removing the effect of atmospheric circulation on temperature and precipitation is tested. The differences in atmospheric circulation as simulated by different models in their control runs and under the historical and representative concentration pathway 8.5 (RCP8.5) forcing scenarios are investigated. Dynamical adjustment is applied to the multimodel ensemble to demonstrate that a substantial fraction of the uncertainty in projected European temperature and precipitation trends is explained by atmospheric circulation variability. A statistically significant response of sea level pressure to anthropogenic forcing is identified in the multimodel ensemble under the RCP8.5 scenario. This forced response in atmospheric circulation is associated with a dynamical contribution to the long-term multimodel mean temperature and precipitation trends. The results highlight the importance of accounting for the impact of atmospheric circulation variability on trends in regional climate projections. [ABSTRACT FROM AUTHOR]

The recent dryness in California was unprecedented in the instrumental record. This article employs spatially explicit precipitation reconstructions for California in combination with instrumental data to provide perspective on this event since 1571. The period 2012-15 stands out as particularly extreme in the southern Central Valley and south coast regions. which likely experienced unprecedented precipitation deficits over this time, apart from considerations of increasing temperatures and drought metrics that combine temperature and moisture information. Some areas lost more than two years' average moisture delivery during these four years, and full recovery to long-term average moisture delivery could typically take up to several decades in the hardest-hit areas. These results highlight the value of the additional centuries of information provided by the paleo record, which indicates the shorter instrumental record may underestimate the statewide recovery time by over 30%. The extreme El Niño that occurred in 2015/16 ameliorated recovery in much of the northern half of the state, and since 1571 very-strong-to-extreme El Niños during the first year after a 2012-15-type event reduce statewide recovery times by approximately half. The southern part of California did not experience the high precipitation anticipated, and the multicentury analysis suggests the north-wet-south-dry pattern for such an El Niño was a low-likelihood anomaly. Recent wetness in California motivated evaluation of recovery times when the first two years are relatively wet, suggesting the state is benefiting from a one-in-five (or lower) likelihood situation: the likelihood of full recovery within two years is ~1% in the instrumental data and even lower in the reconstruction data. [ABSTRACT FROM AUTHOR]

This paper proposes a regularized regression procedure for finding a predictive relation between one variable and a field of other variables. The procedure estimates a linear prediction model under the constraint that the regression coefficients have smooth spatial structure. The smoothness constraint is imposed using a novel approach based on the eigenvectors of the Laplace operator over the domain, which results in a constrained optimization problem equivalent to either ridge regression or least absolute shrinkage and selection operator (LASSO) regression, which can be solved by standard numerical software. In addition, this paper explores an unconventional procedure whereby regression models are estimated from dynamical model output and then verified against observations-the reverse of the traditional order. The methodology is illustrated by constructing statistical prediction models of summer Texas-area temperature based on concurrent Pacific sea surface temperature (SST). None of the regularized regression models have statistically significant skill when estimated from observations. In contrast, when estimated from dynamical model output, the regression models have skill with respect to dynamical model data because of the substantially larger sample size available from dynamical model output. In addition, the regression models estimated from dynamical model data can predict observed anomalies with significant skill, even though no observations were used directly to estimate the regression models. The results indicate that dynamical models had no significant skill because they could not accurately predict the SST itself, not because they could not capture realistic SST teleconnections. [ABSTRACT FROM AUTHOR]

Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC = P − ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern. Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P − ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979-2012 ranging from 0.07 to −0.03 mm day−1 decade−1 are reduced by the adjustments to 0.016 mm day−1 decade−1, much closer to the LSM P − ET estimate (0.007 mm day−1 decade−1). Neither is significant at the 90% level. ENSO-related modulation of VMFC and P − ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86. [ABSTRACT FROM AUTHOR]

Multiproxy warm season (September-February) temperature reconstructions are presented for the combined land-ocean region of Australasia (0°-50°S, 110°E-180°) covering 1000-2001. Using between 2 (R2) and 28 (R28) paleoclimate records, four 1000-member ensemble reconstructions of regional temperature are developed using four statistical methods: principal component regression (PCR), composite plus scale (CPS), Bayesian hierarchical models (LNA), and pairwise comparison (PaiCo). The reconstructions are then compared with a three-member ensemble of GISS-E2-R climate model simulations and independent paleoclimate records. Decadal fluctuations in Australasian temperatures are remarkably similar between the four reconstruction methods. There are, however, differences in the amplitude of temperature variations between the different statistical methods and proxy networks. When the R28 network is used, the warmest 30-yr periods occur after 1950 in 77% of ensemble members over all methods. However, reconstructions based on only the longest records (R2 and R3 networks) indicate that single 30- and 10-yr periods of similar or slightly higher temperatures than in the late twentieth century may have occurred during the first half of the millennium. Regardless, the most recent instrumental temperatures (1985-2014) are above the 90th percentile of all 12 reconstruction ensembles (four reconstruction methods based on three proxy networks-R28, R3, and R2). The reconstructed twentieth-century warming cannot be explained by natural variability alone using GISS-E2-R. In this climate model, anthropogenic forcing is required to produce the rate and magnitude of post-1950 warming observed in the Australasian region. These paleoclimate results are consistent with other studies that attribute the post-1950 warming in Australian temperature records to increases in atmospheric greenhouse gas concentrations. [ABSTRACT FROM AUTHOR]

A multichannel singular spectrum analysis (MSSA) applied simultaneously to tropical sea surface temperature (SST), zonal wind, and burstiness (zonal wind variability) reveals three significant oscillatory modes. They all show a strong ENSO signal in the eastern Pacific Ocean (PO) but also a substantial SST signal in the western Indian Ocean (IO). A correlation-based analysis shows that the western IO signal contains linearly independent information on ENSO. Of the three Indo-Pacific ENSO modes of the MSSA, one resembles a central Pacific (CP) El Niño, while the others represent eastern Pacific (EP) El Niños, which either start in the central Pacific and grow eastward (EPe) or start near Peru and grow westward (EPw). A composite analysis shows that EPw El Niños are preceded by cooling in the western IO about 15 months earlier. Two mechanisms are discussed by which the western IO might influence ENSO. In the atmospheric bridge mechanism, subsidence over the cool western IO in autumn (year 0) leads to enhanced convection above Indonesia, strengthening easterlies over the western PO, and the creation of a large warm water volume. This is essential for the creation of (EP) El Niños in the following spring-summer. In the state-dependent noise mechanism, a cool western IO favors a strong intraseasonal zonal wind variability over the western PO in early spring (year 1), which can partly be attributed to the Madden-Julian oscillation. This intraseasonal variability induces Kelvin waves, which in early spring lead to a strong warming of the eastern PO and can initiate EPw El Niños. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA), also known as empirical orthogonal function (EOF) analysis, is widely used for compression of high-dimensional datasets in such applications as climate diagnostics and seasonal forecasting. A critical question when using this method is the number of modes, representing meaningful signal, to retain. The resampling-based 'Rule N' method attempts to address the question of PCA truncation in a statistically principled manner. However, it is only valid for the leading (largest) eigenvalue, because it fails to condition the hypothesis tests for subsequent (smaller) eigenvalues on the results of previous tests. This paper draws on several relatively recent statistical results to construct a hypothesis-test-based truncation rule that accounts at each stage for the magnitudes of the larger eigenvalues. The performance of the method is demonstrated in an artificial data setting and illustrated with a real-data example. [ABSTRACT FROM AUTHOR]

Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered. [ABSTRACT FROM AUTHOR]

This study investigates the association between the Pacific meridional mode (PMM) and tropical cyclone (TC) activity in the western North Pacific (WNP). It is found that the positive PMM phase favors the occurrence of TCs in the WNP while the negative PMM phase inhibits the occurrence of TCs there. Observed relationships are consistent with those from a long-term preindustrial control experiment (1000 yr) of a high-resolution TC-resolving Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model. The diagnostic relationship between the PMM and TCs in observations and the model is further supported by sensitivity experiments with FLOR. The modulation of TC genesis by the PMM is primarily through the anomalous zonal vertical wind shear (ZVWS) changes in the WNP, especially in the southeastern WNP. The anomalous ZVWS can be attributed to the responses of the atmosphere to the anomalous warming in the northwestern part of the PMM pattern during the positive PMM phase, which resembles a classic Matsuno-Gill pattern. Such influences on TC genesis are strengthened by a cyclonic flow over the WNP. The significant relationship between TCs and the PMM identified here may provide a useful reference for seasonal forecasting of TCs and interpreting changes in TC activity in the WNP. [ABSTRACT FROM AUTHOR]

ENSO-like variability is examined using a set of univariate indices based on unfiltered monthly global sea surface temperature (SST), sea level pressure (SLP), outgoing longwave radiation (OLR), sea level, and the three-dimensional ocean temperature (OT) fields. These indices, many of which correspond to the leading principal components (PCs) of the respective global fields, are highly correlated with each other. In combination with their spatial regression patterns, they provide a comprehensive description of ENSO-like variability in the atmosphere and ocean across time scales ranging from months to decades, from 1950 onward. The SLP and SST indices are highly correlated with one another back to the late nineteenth century. The interdecadal-scale shifts in the prevailing polarity of ENSO that occurred in the 1940s, the 1970s, and around the year 2000 are clearly evident in low-pass-filtered time series of these indices. On the basis of empirical mode decomposition, ENSO-like variability is partitioned into an interannual 'ENSO cycle,' to which equatorial ocean wave dynamics imparts a distinctive equatorial signature, and a red noise background continuum, most prominent on the interdecadal time scale, which resembles the ENSO-like variability in some models in which the atmosphere is coupled to a slab ocean. The background continuum bears the imprint of the Pacific-North American (PNA) pattern, the leading mode of the Northern Hemisphere wintertime variability of the atmospheric circulation over the Pacific sector. The superposition of the ENSO cycle and the background continuum imparts a distinctive frequency dependence to the patterns of ENSO-like climate variability. [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Sounder (AIRS) level-1b radiances have been shown to be well calibrated (~0.3 K or higher) and have little secular drift (~4 mK yr−1) since operation started in September 2002. This paper investigates the linear trends of 10 years (2003-12) of AIRS global-mean radiances in the CO2 v2 band that are sensitive to emissions from the stratosphere (stratospheric channels). AIRS lower-stratospheric channels have a cooling trend of no more than 0.23 K decade−1 whereas the midstratospheric channels consistently show a statistically significant cooling trend as large as 0.58 K decade−1. The 95% confidence interval for the trend is ~±0.20 K decade−1. Two sets of synthetic AIRS radiances are computed using the principal component-based radiative transfer model (PCRTM), one based on a free-running GFDL Atmospheric Model, version 3 (AM3), over the same period and one based on ERA-Interim. The GFDL AM3 simulations overestimate the cooling trends in the mid- to upper-stratospheric channels but slightly underestimate them in the lower-stratospheric channels. The synthetic radiances based on ERA-Interim, however, have statistically significant positive trends at virtually all stratospheric channels. This confirms the challenge to the GCM modeling and reanalysis community to create a better simulation or assimilation of the stratospheric climate. It is shown that the linear trends in AIRS radiances can be reproduced to a large extent by the spectral radiative kernel technique and the trends from the AIRS L2 temperature retrievals and from the change of CO2. This suggests a closure between AIRS L1 radiances and L2 retrievals and the potential merit of AIRS data in studies of stratosphere changes. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA. [ABSTRACT FROM AUTHOR]

Reanalysis data and radiation budget data are used to calculate the role of the atmospheric cloud radiative effect in determining the magnitude of horizontal export of energy by the tropical atmosphere. Because tropical high clouds result in net radiative heating of the atmosphere, they increase the requirement for the atmosphere to export energy from convective regions. Increases in upper-tropospheric water vapor associated with convection contribute about a fifth of the atmospheric radiative heating anomaly associated with convection. Over the warmest tropical oceans, the radiative effect of convective clouds and associated water vapor is roughly two-thirds the value of the atmospheric energy transport. Cloud radiative heating and atmospheric heat transport increase at the same rate with increasing sea surface temperature, suggesting that the increased energy export is supplied by the radiative heating associated with convective clouds. The net cloud radiative effect at the top of the atmosphere is insensitive to changes in SST over the warm pool. Principal component analysis of satellite-retrieved cloud data reveals that the insensitivity of the net cloud radiative effect to SST is the result of changes in cloud amount offsetting changes in cloud optical thickness and cloud-top height. While increasing upward motion makes the cloud radiative effect more negative, that decrease is offset by reductions in outgoing longwave radiation owing to increases in water vapor. [ABSTRACT FROM AUTHOR]

The 2013/14 boreal winter (December 2013-February 2014) brought extended periods of anomalously cold weather to central and eastern North America. The authors show that a leading pattern of extratropical variability, whose sea level pressure footprint is the North Pacific Oscillation (NPO) and circulation footprint the West Pacific (WP) teleconnection-together, the NPO-WP-exhibited extreme and persistent amplitude in this winter. Reconstruction of the 850-hPa temperature, 200-hPa geopotential height, and precipitation reveals that the NPO-WP was the leading contributor to the winter climate anomaly over large swaths of North America. This analysis, furthermore, indicates that NPO-WP variability explains the most variance of monthly winter temperature over central-eastern North America since, at least, 1979. Analysis of the NPO-WP related thermal advection provides physical insight on the generation of the cold temperature anomalies over North America. Although NPO-WP's origin and development remain to be elucidated, its concurrent links to tropical SSTs are tenuous. These findings suggest that notable winter climate anomalies in the Pacific-North American sector need not originate, directly, from the tropics. More broadly, the attribution of the severe 2013/14 winter to the flexing of an extratropical variability pattern is cautionary given the propensity to implicate the tropics, following several decades of focus on El Niño-Southern Oscillation and its regional and far-field impacts. [ABSTRACT FROM AUTHOR]

Singular spectrum analysis (SSA) along with its multivariate extension (M-SSA) provides an efficient way to identify weak oscillatory behavior in high-dimensional data. To prevent the misinterpretation of stochastic fluctuations in short time series as oscillations, Monte Carlo (MC)-type hypothesis tests provide objective criteria for the statistical significance of the oscillatory behavior. Procrustes target rotation is introduced here as a key method for refining previously available MC tests. The proposed modification helps reduce the risk of type-I errors, and it is shown to improve the test's discriminating power. The reliability of the proposed methodology is examined in an idealized setting for a cluster of harmonic oscillators immersed in red noise. Furthermore, the common method of data compression into a few leading principal components, prior to M-SSA, is reexamined, and its possibly negative effects are discussed. Finally, the generalized Procrustes test is applied to the analysis of interannual variability in the North Atlantic's sea surface temperature and sea level pressure fields. The results of this analysis provide further evidence for shared mechanisms of variability between the Gulf Stream and the North Atlantic Oscillation in the interannual frequency band. [ABSTRACT FROM AUTHOR]

Processes controlling moisture variations associated with the MJO are investigated using budgets of moist static energy (MSE) and moisture. To first order, precipitation anomalies are maintained by anomalous large-scale vertical moisture advection, which can be understood through application of a weak temperature gradient balance framework to the MSE budget. Intraseasonal variations in longwave radiative cooling play a crucial role in destabilizing the MJO by enhancing intraseasonal variations in large-scale vertical moisture advection. This enhancement allows the effect of intraseasonal variations in large-scale vertical moisture advection to meet or exceed the effect of intraseasonal variations in net condensation, resulting in a positive feedback between the net effect of these processes and moisture anomalies. Intraseasonal variations in surface latent heat flux (SLHF) enhance this positive feedback, but appear to be insufficient to destabilize the MJO in the absence of radiative feedbacks. The effect an ensemble cloud population has on large-scale moisture is investigated using fields where only high-frequency variability has been removed. During the enhanced phase, approximately 85% of the moisture removed by net condensation is resupplied by the large-scale vertical moisture advection associated with apparent heating by microphysical processes and subgrid-scale vertical fluxes of dry static energy. This suggests that a relatively large increase in net condensation could be supported by a relatively small anomalous moisture source, even in the absence of radiative feedbacks. These results highlight the importance of process-oriented assessment of MJO-like variability within models, and suggest that a weak temperature gradient (WTG) balance framework may be used to identify destabilization mechanisms, thereby distinguishing between MJO-like variability of fundamentally different character. [ABSTRACT FROM AUTHOR]

One of the most commonly used metrics for both locating the Madden-Julian oscillation (MJO) geographically and defining the intensity of MJO convective activity is the real-time multivariate MJO (RMM) index. However, a climatology of the MJO, particularly with respect to the frequency of activity levels or of consecutive days at certain activity thresholds, does not yet exist. Thus, several climatological aspects of the MJO were developed in this study: 1) annual and 2) seasonal variability in MJO intensity, quantified using four defined activity categories (inactive, active, very active, and extremely active); 3) persistence in the above-defined four categories; 4) cycle length; and 5) low-frequency (decadal) variability. On an annual basis, MJO phases 1 and 2 occurred more often, and phase 8 occurred less often, than the other phases throughout the year. Notable seasonality was also found, particularly in the frequency of extremely active MJO in March-May (8% of days) compared with June-August (only 1% of days). The MJO was persistent in time and across intensity categories, and all activity categories the following day had at least an 80% chance of maintaining their amplitudes. Implications of this climatology are discussed, including length of complete MJO cycles (the shortest of which was 17 days) and correlations between MJO amplitude and atmospheric response. [ABSTRACT FROM AUTHOR]

Because of the importance of clouds in modulating Earth's energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast for CA and in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño-Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supports the conclusion that they are describing cloud variations with these climate modes. [ABSTRACT FROM AUTHOR]

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin-Neelin-Ghil (JNG) hybrid seasonally forced coupled ocean-atmosphere model of El Niño-Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample. [ABSTRACT FROM AUTHOR]

This study examines the changes in tropical cyclone (TC) precipitation and the associated contributing factors over southeast China during 1960-2009. Climatologically, TC rainfall accounts for approximately 20%-40% of the total rainfall over southeast China during boreal summer, and the contribution can even reach 50% for some of the coastal provinces, such as Guangdong, Fujian, Zhejiang, and Hainan. The dominant mode of TC rainfall reveals a dipole pattern over southern southeast China (SSC) and eastern southeast China (ESC), and the associated principal component time series exhibits remarkable interdecadal variations, with two potential change points being identified in the late 1970s and early 1990s. These interdecadal shifts in TC rainfall are also found to be synchronous with two regime shifts in total rainfall, and they can account for more than 40% of the total rainfall anomalies over the coastal regions of southeast China. To discover the dominant factors responsible for the interdecadal variations, the overall TC rainfall anomalies are broken down into three different components (rainfall frequency, rainfall intensity, and nonlinear terms) based on a new empirical statistical approach. It is found that the interdecadal variation in TC precipitation over SSC is controlled predominantly by changes in TC rainfall intensity as well as TC rainfall frequency, while that over ESC depends mainly on the intensity and the nonlinear terms. Further examination of the TC passage frequency (TPF) suggests that the significant reduction in TPF and TC rainfall frequency over SSC during 1979-92 is associated mainly with suppressed TC genesis (negative genesis effect), while the increase in TPF and TC rainfall frequency during 1993-2009 can be attributed primarily to the enhanced passage probability (positive track effect) over SSC. Meanwhile, variations in TC rainfall intensity seem to be unrelated to the TC's own intensity change. [ABSTRACT FROM AUTHOR]

Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948-2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a 'geography of extremes' based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region. [ABSTRACT FROM AUTHOR]

The vertical structure of tropical deep convection strongly influences interactions with larger-scale circulations and climate. This paper focuses on investigating this vertical structure and its relationship with mesoscale tropical weather states. The authors test the hypothesis that latent heating plus turbulent flux convergence varies (in space and time) in association with weather state type. The authors estimate mean-state vertical motion profile shape and latent heating plus turbulent flux convergence for six weather states defined using cloud-top pressure and optical depth properties from the International Satellite Cloud Climatology Project (ISCCP) dataset. Assuming two modes of vertical motion profile variability, these modes are statistically extracted from reanalysis data using a principal component analysis. Using these modes and the relationship between vertical motion, the dry static energy budget, and mass continuity, the authors estimate vertical motion profile shape. In these estimates, the authors use Global Precipitation Climatology Project (GPCP) [and Tropical Rainfall Measuring Mission (TRMM) 3B42] precipitation and Quick Scatterometer (QuikSCAT) surface convergence data in the ITCZ region from 2001 to 2006. Finally, these profile shapes are categorized by weather state type and spatiotemporally averaged to generate mean-state vertical motion profiles and latent heating plus turbulent flux convergence. The authors find that vertical motion profile shape varies by weather state. The isolated systems convective regime exhibits more 'bottom heaviness' than the other convectively active regimes, with maximum upward vertical motion occurring in the lower troposphere rather than the middle to upper troposphere. The variability observed does not coincide with the conventional profile variability based on stratiform rain fraction. [ABSTRACT FROM AUTHOR]

This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies. [ABSTRACT FROM AUTHOR]

Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958-2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November-April) there is a positive relationship with the El Niño-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May-October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS. A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers. [ABSTRACT FROM AUTHOR]

The efficacy of a novel ensemble data assimilation (DA) technique is examined in the climate field reconstruction (CFR) of surface temperature. A minimalistic, computationally inexpensive DA technique is employed that requires only a static ensemble of climatologically plausible states. Pseudoproxy experiments are performed with both general circulation model (GCM) and Twentieth Century Reanalysis (20CR) data by reconstructing surface temperature fields from a sparse network of noisy pseudoproxies. The DA approach is compared to a conventional CFR approach based on principal component analysis (PCA) for experiments on global domains. DA outperforms PCA in reconstructing global-mean temperature in all experiments and is more consistent across experiments, with a range of time series correlations of 0.69-0.94 compared to 0.19-0.87 for the PCA method. DA improvements are even more evident in spatial reconstruction skill, especially in sparsely sampled pseudoproxy regions and for 20CR experiments. It is hypothesized that DA improves spatial reconstructions because it relies on coherent, spatially local temperature patterns, which remain robust even when glacial states are used to reconstruct nonglacial states and vice versa. These local relationships, as utilized by DA, appear to be more robust than the orthogonal patterns of variability utilized by PCA. Comparing results for GCM and 20CR data indicates that pseudoproxy experiments that rely solely on GCM data may give a false impression of reconstruction skill. [ABSTRACT FROM AUTHOR]

Empirical orthogonal function (EOF) analysis is commonly used in the climate sciences and elsewhere to describe, reconstruct, and predict highly dimensional data fields. When data contain a high percentage of missing values (i.e., gappy), alternate approaches must be used in order to correctly derive EOFs. The aims of this paper are to assess the accuracy of several EOF approaches in the reconstruction and prediction of gappy data fields, using the Galapagos Archipelago as a case study example. EOF approaches included least squares estimation via a covariance matrix decomposition [least squares EOF (LSEOF)], data interpolating empirical orthogonal functions (DINEOF), and a novel approach called recursively subtracted empirical orthogonal functions (RSEOF). Model-derived data of historical surface chlorophyll- a concentrations and sea surface temperature, combined with a mask of gaps from historical remote sensing estimates, allowed for the creation of true and observed fields by which to gauge the performance of EOF approaches. Only DINEOF and RSEOF were found to be appropriate for gappy data reconstruction and prediction. DINEOF proved to be the superior approach in terms of accuracy, especially for noisy data with a high estimation error, although RSEOF may be preferred for larger data fields because of its relatively faster computation time. [ABSTRACT FROM AUTHOR]