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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]

Identifying and separating the signal of urbanization effects in current temperature data series is essential for accurately detecting, attributing, and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based on the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951–2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the mean and extreme temperature indices series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual mean and extreme temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades. [ABSTRACT FROM AUTHOR]

Conventional analysis methods in weather and climate science (e.g., EOF analysis) exhibit a number of drawbacks including scaling and mixing. These methods focus mostly on the bulk of the probability distribution of the system in state space and overlook its tail. This paper explores a different method, the archetypal analysis (AA), which focuses precisely on the extremes. AA seeks to approximate the convex hull of the data in state space by finding 'corners' that represent 'pure' types or archetypes through computing mixture weight matrices. The method is quite new in climate science, although it has been around for about two decades in pattern recognition. It encompasses, in particular, the virtues of EOFs and clustering. The method is presented along with a new manifold-based optimization algorithm that optimizes for the weights simultaneously, unlike the conventional multistep algorithm based on the alternating constrained least squares. The paper discusses the numerical solution and then applies it to the monthly sea surface temperature (SST) from HadISST and to the Asian summer monsoon (ASM) using sea level pressure (SLP) from ERA-40 over the Asian monsoon region. The application to SST reveals, in particular, three archetypes, namely, El Niño, La Niña, and a third pattern representing the western boundary currents. The latter archetype shows a particular trend in the last few decades. The application to the ASM SLP anomalies yields archetypes that are consistent with the ASM regimes found in the literature. Merits and weaknesses of the method along with possible future development are also discussed. [ABSTRACT FROM AUTHOR]

Interannual and climatological variations of wind stress drag coefficient (CD) are examined over the global ocean from 1998 to 2004. Here CD is calculated using high temporal resolution (3- and 6-hourly) surface atmospheric variables from two datasets: 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and 2) the Navy Operational Global Atmospheric Prediction System (NOGAPS). The stability-dependent CD algorithm applied to both datasets gives almost identical values over most of the global ocean, confirming the validity of results. Overall, major findings of this paper are as follows: 1) the CD value can change significantly (e.g., >50%) on 12-hourly time scales around the Kuroshio and Gulf Stream current systems; 2) there is strong seasonal variability in CD, but there is not much interannual change in the spatial variability for a given month; 3) a global mean CD ≈ 1.25 × 10-3 is found in all months, while CD ≥ 1.5 × 10-3 is prevalent over the North Pacific and North Atlantic Oceans and in southern high-latitude regions as well, and CD ≤ 1.0 × 10-3 is typical in the eastern equatorial Pacific cold tongue; and 4) including the effects of air–sea stability on CD generally causes an increase of >20% in comparison to the one calculated based on neutral conditions in the tropical regions. Finally, spatially and temporally varying CD fields are therefore needed for a variety of climate and air–sea interaction studies. [ABSTRACT FROM AUTHOR]

This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network-Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT's higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations. [ABSTRACT FROM AUTHOR]

Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA's monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth's and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA's GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century−1 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900. Significance Statement: Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century−1 greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively. [ABSTRACT FROM AUTHOR]

In a recent paper, Christiansen et al. compared climate reconstruction methods using surrogate ensembles from a coupled general circulation model and pseudoproxies. Their results using the regularized expectation maximization method with truncated total least squares (RegEM-TTLS) appear inconsistent with previous studies. Results presented here show that the poor performance of RegEM-TTLS in Christiansen et al. is due to 1) their use of the nonhybrid method compared to the hybrid method; 2) a stagnation tolerance that is too large and does not permit the solution to stabilize, which is compounded in another paper by Christiansen et al. by the introduction of an inappropriate measure of stagnation; and 3) their use of a truncation parameter that is too large. Thus, the poor performance of RegEM-TTLS in both Christiansen et al. papers is due to poor implementation of the method rather than to shortcomings inherent to the method. [ABSTRACT FROM AUTHOR]

In this paper a 55-yr (1948–2002) Northern Hemisphere blocking climatology is presented. Traditional blocking indices and methodologies are revised and a new blocking detection method is designed. This algorithm detects blocked flows and provides for a better characterization of blocking events with additional information on blocking parameters such as the location of the blocking center, the intensity, and extension. Additionally, a new tracking procedure has been incorporated following simultaneously the individual evolution of blocked flows and identifying coherently persistent blocked patterns. Using this method, the longest known Northern Hemisphere blocking climatology is obtained and compared with previous studies. A new regional classification into four independent blocking sectors has been obtained based on the seasonally preferred regions of blocking formation: Atlantic (ATL), European (EUR), West Pacific (WPA), and East Pacific (EPA). Global and regional blocking characteristics have been described, examining their variability from the seasonal to interdecadal scales. The global long-term blocking series in the North Hemisphere showed a significant trend toward weaker and less persistent events, as well as regional increases (decreases) in blocking frequency over the WPA (ATL and EUR) sector. The influence of teleconnection patterns (TCPs) on blocking parameters is also explored, being confined essentially to wintertime, except in the WPA sector. Additionally, regional blocking parameters, especially frequency and duration, are sensitive to regional TCPs, supporting the regional classification obtained in this paper. The ENSO-related blocking variability is evident in blocking intensities and preferred locations but not in frequency. Finally, the dynamical connection between blocking occurrence and regional TCPs is examined through the conceptual model proposed by Charney and DeVore. Observational evidence of a dynamical link between the asymmetrical temperature distributions induced by TCPs and blocking variability is provided with a distinctive contrast “warm ocean/cold land” pattern favoring the blocking occurrence in winter. However, the conceptual model is not coherent in the WPA sector, suggesting different blocking mechanisms operating in this sector. [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]

There are increasing efforts to use climate model output for adaptation planning, but meanwhile there is often limited understanding of how models represent regional climate. Here we analyze the simulation in global coupled climate models of a key rainfall-generating mechanism over southern Africa: tropical temperate troughs (TTTs). An image-processing algorithm is applied to outgoing longwave radiation data from satellites and models to create TTT event sets. All models investigated produce TTTs with similar circulation features to observed. However, there are large differences among models in the number, intensity, and preferred longitude of events. Five groups of models are identified. The first group generates too few TTTs, and relatively dry conditions over southern Africa compared to other models.Asecond group generates more TTTs and wet biases. The contrast between these two groups suggests that the number of TTTs could explain intermodel variations in climatological rainfall. However, there is a third group of models that simulate up to 92% more TTTs than observed, but do not have large rainfall biases, as each TTT event is relatively weak. Finally, there are a further two groups that concentrate TTTs over the subcontinent or the ocean, respectively. These distinctions between models are associated with the amount of convective activity in the Congo Basin, the magnitude of moisture fluxes into southern Africa, and the degree of zonal asymmetry in upper-level westerly flow. Model development focused on tropical convection and the representation of orography is needed for improved simulation of TTTs, and therefore southern African rainfall. [ABSTRACT FROM AUTHOR]

This paper compares three existing Palmer Drought Severity Index (PDSI) formulations for simulating summer moisture variability in western Canada and a preliminary analysis of climate change impacts on summer moisture anomalies. The three models considered are Palmer's original algorithm (orPDSI), the self-calibrating PDSI (scPDSI), and a version modified for Canadian Prairie conditions (cpPDSI). In all formulations, potential evapotranspiration was parameterized by the Penman-Monteith method instead of the traditional Thornthwaite method. The scPDSI was used as a benchmark for evaluation as it is more appropriate for comparing drought severity of diverse climates. The results confirm that orPDSI produces inflated drought statistics as compared to scPDSI, whereas cpPDSI produced more conservative drought statistics than scPDSI. On the basis of results from scPDSI, historical moisture availability in the Canadian Prairies has shown a significant downward trend since 1950 at the 5% level, whereas southern British Columbia has shown a significant increasing trend. No discernible trend was found in the northern parts of the study area. These results were corroborated by trends in annual precipitation and summer temperature over the respective regions. When scPDSI parameters were calibrated using historical climate data, simulations for the 2050s using climate change scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) showed increases in summer moisture deficit relative to the 1961-90 baseline. However, projecting the extent to which the frequency of extreme drought and/or wet spell categories will change is not trivial since the computation of scPDSI is tied to the definition of the frequency of extreme events. [ABSTRACT FROM AUTHOR]

This paper studies genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series. Such methods bypass classic subsegmentation algorithms, which sometimes yield suboptimal conclusions. Minimum description length techniques are introduced. These techniques require optimizing an objective function over all possible changepoint numbers and location times. The general objective functions allow for correlated data, reference station aspects, and/or nonnormal marginal distributions, all common features of climate time series. As an exhaustive evaluation of all changepoint configurations is not possible, the optimization is accomplished via a genetic algorithm that randomly walks through a subset of good models in an intelligent manner. The methods are applied in the analysis of 173 yr of annual precipitation measurements from New Bedford, Massachusetts, and the North Atlantic basin tropical cyclone record. [ABSTRACT FROM AUTHOR]

The paper presents an approach for conditional airmass classification based on local precipitation rate distributions. The method seeks, within the potential region, three-dimensional atmospheric predictor domains with high impact on the local-scale phenomena. These predictor domains are derived by an algorithm consisting of a clustering method, namely, self-organizing maps, and a nonlinear optimization method, simulated annealing. The findings show that the resulting spatial structures can be attributed to well-known atmospheric processes. Since the optimized predictor domains probably contain relevant information for precipitation generation, these grid points may also be potential inputs for nonlinear downscaling methods. Based on this assumption, the potential of these optimized large-scale predictors for downscaling has been investigated by applying an artificial neural network as a nonparametric statistical downscaling model. Compared to preset local predictors, using the optimized predictors improves the accuracy of the downscaled time series, particularly in summer and autumn. However, optimizing predictors by a conditional classification does not guarantee that a predictor increases the explained variance of the downscaling model. To study the contribution of each predictor to the output variance, either individually or by interactions with other parameters, the sources of uncertainty have been estimated by global sensitivity analysis, which provides model-free sensitivity measures. It is shown that predictor interactions play an important part in the modeling process and should be taken into account in the predictor screening. [ABSTRACT FROM AUTHOR]

The purpose of this study is twofold: to (i) establish three-dimensional (3D) longwave radiative transfer benchmarks for inhomogeneous cloud fields and (ii) compare the results with three approximate, 1D methods. The benchmark results are calculated using a correlated- k three-dimensional Monte Carlo (3DMC) algorithm that is validated via comparisons to line-by-line calculations for simple atmospheres. The approximate methods include an independent column approximation (ICA) and two cloud-overlap schemes: maximum/random (MRO) and random (RO). Six inhomogeneous cloudy-sky test cases are used and encompass a wide range of domain sizes used by general circulation models. Domain-averaged fluxes and heating rates from these atmospheres show that the ICA is consistently more accurate than the cloud-overlap models with respect to the 3D benchmarks. For example, comparisons of model results for the Atlantic Trade Wind Experiment (ATEX), a marine boundary layer cumulus field, yield a maximum cloud-layer heating rate error of 15.73 K day−−1 from using cloud-overlap models, whereas the ICA error is only 2.17 K day−−1. This paper presents results showing that these differences are attributed to the 3D effects of unresolved clouds and indicate that there is an inherent deficiency in the ability of 1D models to accurately calculate radiative quantities in these atmospheres. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate two satellite instruments for SST: the infrared (IR) Advanced Along Track Scanning Radiometer (AATSR) and the microwave (MW) Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Because of its dual view, AATSR has a potential for lower biases than other IR products such as the Advanced Very High Resolution Radiometer (AVHRR), while the tropical TMI record was available for a longer period of time than the global MW instrument, the Advanced Microwave Scanning Radiometer (AMSR). The results show that the AATSR IR retrievals are good quality with biases lower than or as low as other satellite retrievals between 50°S and 50°N. Furthermore, the dual-view algorithm reduces the influence of aerosol contamination. However, the AATSR coverage is roughly half that of AVHRR. North of 50°N there appear to be biases and high variability in summer daytime retrievals, with smaller but consistent biases observed below 50°S. TMI data can significantly improve coverage offshore in regions where IR retrievals are reduced by cloud cover. However, TMI data have small-scale biases from land contamination that should be removed by modifying the land–sea mask to remove more coastal regions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the influence of tropical sea surface temperature (SST) on the midlatitude circulation regime behavior in the Pacific–North American region. Toward this end, a cluster analysis has been applied to 55-member ensembles of winter seasonal simulations of the Center for Ocean–Land–Atmosphere Studies general circulation model for 18 winters. The ensemble members for each winter utilize the same prescribed, observed weekly varying SST for that winter. The cluster analysis includes all fluctuations with time scales longer than 10 days except for the ensemble mean seasonal cycle. Using a partitioning algorithm separately applied to each winter's ensemble, clusters are found in the 200-hPa height field that are signicant (vis-à-vis a suitable Gaussian background), reproducible (in half-length datasets), and consistent (with clusters obtained from the 200-hPa u wind) for all winters except the strong El Niño events of 1982/83, 1986/87, and 1997/98. One cluster found consistently in many winters, consisting of a strong ridge over the Alaskan region and a trough over central North America, is quite similar to the Alaskan pattern identified from observations as being particularly difficult to predict and that occurs preferentially during La Niña events. Two other clusters found in many winters have no observational counterparts. A regime that is very similar to the seasonal mean response to cold tropical Pacific SSTs is seen during several La Niña winters. A strong negative correlation between a measure of the strength of the clustering and the Niño-3 SST index is found. That this correlation is as strong as the correlation between the seasonal mean response and the same SST index indicates that the El Niño–Southern Oscillation–related SSTs affect the regime structure of intraseasonal flow as strongly as they do the mean state. [ABSTRACT FROM AUTHOR]

Global warming intensifies atmospheric water vapor transport between ocean and land, which increases the likelihood of extreme precipitation and floods. However, accurate estimations of water vapor exchange between ocean and land are difficult due to the lack of available data and effective methods. This study developed a novel eight-direction-vector decomposition algorithm for calculating water vapor flux between ocean and land based on the ERA5 dataset, and the results showed that global water vapor exchange between ocean and land had significantly increased in the past 40 years, except for Antarctica. During 1980–2018, the average annual net water vapor inflow from ocean to land (Qnet) was 44.68 × 1015 kg yr−1, and Qnet increased at a rate of 1.48 × 1015 kg yr−1 decade−1. The intensified atmospheric water vapor exchange between ocean and land was directly caused by the increase of atmospheric water vapor content, which largely depended on the rising air temperature, and it was found that water vapor flux between ocean and land increased by over 8% K−1 with the increasing air temperature at the global average. This study also identified El Niño–Southern Oscillation (ENSO) as an important contributor to the global ocean–land water vapor exchange anomalies. A strong El Niño event (MEI = 1) can result in a 1.36 × 1015 kg yr−1 (3.03%) decrease in Qnet, and a strong La Niña event (MEI = −1) can increase Qnet by 1.38 × 1015 kg yr−1 (3.09%). The eight-direction-vector decomposition algorithm was effective in ocean–land water vapor flux estimations at different spatial and temporal scales, which could provide great insights into the mechanisms of extreme precipitation events. Significance Statement: This study developed a novel approach on water vapor flux estimation (i.e., the eight-direction-vector decomposition algorithm) and achieved a high-temporal–spatial-resolution estimation of water vapor flux between ocean and land. It was found that water vapor flux between ocean and land was intensified by increasing air temperature at a rate of 8% K−1, and El Niño yielded an anomaly low net water vapor input from ocean to land at the global scale. The algorithm developed in this study can be used for estimating water vapor fluxes at different spatial and temporal scales, which is crucial for evaluating the role of water vapor flux on formations for extreme weather (e.g., torrential rainstorms and heat waves) and climatic extremes (e.g., droughts and floods). [ABSTRACT FROM AUTHOR]

A correction to the article "An objective satellite-based tropical cyclone size climatology" by J. A. Knaff and colleagues that was published in the previous issue is presented.

Extreme precipitation across multiple time scales is a natural hazard that creates a significant risk to life, with a commensurately large cost through property loss. We devise a method to create 14-day extreme-event windows that characterize precipitation events in the contiguous United States (CONUS) for the years 1915–2018. Our algorithm imposes thresholds for both total precipitation and the duration of the precipitation to identify events with sufficient length to accentuate the synoptic and longer time scale contribution to the precipitation event. Kernel density estimation is employed to create extreme-event polygons that are formed into a database spanning from 1915 through 2018. Using the developed database, we clustered events into regions using a k-means algorithm. We define the "hybrid index," a weighted composite of silhouette score and number of clustered events, to show that the optimal number of clusters is 15. We also show that 14-day extreme precipitation events are increasing in the CONUS, specifically in the Dakotas and much of New England. The algorithm presented in this work is designed to be sufficiently flexible to be extended to any desired number of days on the subseasonal-to-seasonal (S2S) time scale (e.g., 30 days). Additional databases generated using this framework are available for download from our GitHub. Consequently, these S2S databases can be analyzed in future works to determine the climatology of S2S extreme precipitation events and be used for predictability studies for identified events. [ABSTRACT FROM AUTHOR]

This study uses machine-learning methods, specifically the random-forests (RF) method, on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004–16. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, along with reanalysis environmental information from a 5-yr manually identified nonlinear mode and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, of which one-half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (March–October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus they have higher potential for producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs. [ABSTRACT FROM AUTHOR]

Investigations of variations and causes of near-surface wind speed (NWS) further understanding of atmospheric changes and improve the ability of climate analysis and projections. NWS varies on multiple temporal scales; however, the centennial-scale variability in NWS and associated causes over China remains unknown. In this study, we employ the European Centre for Medium-Range Weather Forecasts (ECMWF) Twentieth Century Reanalysis (ERA-20C) to study the centennial-scale changes in NWS from 1900 to 2010. Meanwhile, a forward stepwise regression algorithm is used to reveal the relationships between NWS and large-scale ocean–atmosphere circulations. The results show three unique periods in annual mean NWS over China from 1900 to 2010. The annual mean NWS displayed decreasing trends of −0.87% and −11.75% decade−1 from 1900 to 1925 and from 1957 to 2010, respectively, which were caused by the decreases in the days with strong winds, with trends of −6.64 and −4.66 days decade−1, respectively. The annual mean NWS showed an upward trend of 55.47% decade−1 from 1926 to 1956, which was caused by increases in the days with moderate (0.43 days decade−1) and strong winds (23.55 days decade−1). The reconstructed wind speeds based on forward stepwise regression algorithm matched well with the original wind speeds; therefore, the decadal changes in NWS over China at the centennial scale were mainly induced by large-scale ocean–atmosphere circulations, with the total explanation power of 66%. The strongest explanation power was found in winter (74%), and the weakest explanation power was found in summer (46%). [ABSTRACT FROM AUTHOR]

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish "MULTITEST" project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), "Climatol," multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and "RHtests"—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended. [ABSTRACT FROM AUTHOR]

Recent enhanced warming and sea ice depletion in the Arctic have been put forward as potential drivers of severe weather in the midlatitudes. Evidence of a link between Arctic warming and midlatitude atmospheric circulation is growing, but the role of Arctic processes relative to other drivers remains unknown. Arctic–midlatitude connections in the North Atlantic region are particularly complex but important due to the frequent occurrence of severe winters in recent decades. Here, dynamic Bayesian networks with hidden variables are introduced to the field to assess their suitability for teleconnection analyses. Climate networks are constructed to analyze North Atlantic circulation variability at 5-day to monthly time scales during the winter months of the years 1981–2018. The inclusion of a number of Arctic, midlatitude, and tropical variables allows for an investigation into the relative role of Arctic influence compared to internal atmospheric variability and other remote drivers. A robust covariability between regions of amplified Arctic warming and two definitions of midlatitude circulation is found to occur entirely within winter at submonthly time scales. Hidden variables incorporated in networks represent two distinct modes of stratospheric polar vortex variability, capturing a periodic shift between average conditions and slower anomalous flow. The influence of the Barents–Kara Seas region on the North Atlantic Oscillation is found to be the strongest link at 5- and 10-day averages, while the stratospheric polar vortex strongly influences jet variability on monthly time scales. [ABSTRACT FROM AUTHOR]

Space-based measurements of the outgoing longwave radiation (OLR) are essential for the study of Earth’s climate system. While the CERES instrument provides accurate measurements of this quantity, its measurements are not spectrally resolved. Here we present a high-resolution OLR product (sampled at 0.25 cm−1), derived from measurements of the IASI satellite sounder. The applied methodology relies on precalculated angular distribution models (ADMs). These are usually calculated for tens to hundreds of different scene types (characterized by surface and atmosphere parameters). To guarantee accurate results in the range 645–2300 cm−1 covered by IASI, we constructed ADMs for over 140 000 scenes. These were selected from one year of CAMS reanalysis data. A dissimilarity-based selection algorithm was applied to choose scenes as different from each other as possible, thereby maximizing the performance on real data, while keeping the number of scenes manageable. A comparison of the IASI OLR integrated over the 645–2300 cm−1 range was performed with the longwave broadband OLR products from CERES and the AIRS instrument. The latter are systematically higher due to the contribution of the far infrared to the total IR spectral range, but as expected exhibit generally high spatial correlations with the IASI OLR, except for some areas in the tropical region. We also compared the IASI OLR against the spectrally resolved OLR derived from AIRS. A good agreement was found above 1200 cm−1 while AIRS OLR appeared to be systematically higher in the atmospheric window region, likely related to differences in overpass time or to the use of a different cloud detection algorithm. [ABSTRACT FROM AUTHOR]

A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise. [ABSTRACT FROM AUTHOR]

Australian northwest cloudbands (NWCBs) are continental-scale bands of continuous cloud that stretch from northwest to southeast Australia. In earlier studies, where the characteristics of NWCBs and their relationship with precipitation were identified from satellite imagery, there was considerable uncertainty in the results due to limited quality and availability of data. The present study identifies NWCBs from 31 years of satellite data using a pattern-matching algorithm. This new climatology is the longest record based entirely on observations. Our findings include a strong annual cycle in NWCB frequency, with a summer maximum and winter minimum, and a statistically significant increase in annual NWCB days over the period 1984–2014. Physical mechanisms responsible for NWCB occurrences are explored to determine whether there is a fundamental difference between summer and winter NWCBs as hypothesized in earlier studies. Composite analyses are used to reveal that a key difference between these is their genesis mechanisms. Whereas summer NWCBs are triggered by cyclonic disturbances, winter NWCBs tend to form when meridional sea surface temperature gradients trigger baroclinic instability. It was also found that while precipitation is enhanced over parts of Australia during a cloudband day, it is reduced in other regions. During a cloudband day, precipitation extremes are more likely over northwest, central, and southeast Australia, while the probability of extreme precipitation decreases in northeast and southwest Australia. Additionally, cold fronts and NWCBs can interact, leading to enhanced rainfall over Australia. [ABSTRACT FROM AUTHOR]

Understanding the dynamics and physics of climate extremes will be a critical challenge for twenty-first-century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts, and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016, the number of observations in the University of East Anglia Climatic Research Unit (CRU) Tmax product declined precipitously (5900 → 1000); 1000 poorly distributed measurements are insufficient to resolve regional Tmax variations. Here, we show that combining long (1983 to the near present), high-resolution (0.05°), cloud-screened archives of geostationary satellite thermal infrared (TIR) observations with a dense set of ~15 000 station observations explains 23%, 40%, 30%, 41%, and 1% more variance than the CRU globally and for South America, Africa, India, and areas north of 50°N, respectively; even greater levels of improvement are shown for the 2011–16 period (28%, 45%, 39%, 52%, and 28%, respectively). Described here for the first time, the TIR Tmax algorithm uses subdaily TIR distributions to screen out cloud-contaminated observations, providing accurate (correlation ≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15 000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicate that the satellite-only, station-only, and combined products all perform accurately (R ≈ 0.8–0.9, mean absolute errors ≈ 0.8–1.0). Hence, the Climate Hazards Center Infrared Temperature with Stations (CHIRTSmax) dataset should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications. [ABSTRACT FROM AUTHOR]

Ten years of terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) were used to estimate high-latitude snowfall accumulation using a mass balance approach. The estimates were used to assess two common gauge-undercatch correction factors (CFs): the Legates climatology (CF-L) utilized in the Global Precipitation Climatology Project (GPCP) and the Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) monitoring product. The two CFs can be different by more than 50%. CF-L tended to exceed CF-F over northern Asia and Eurasia, while the opposite was observed over North America. Estimates of snowfall from GPCP, GPCC-L (GPCC corrected by CF-L), and GPCC-F (GPCC corrected by CF-F) were 62%, 64%, and 46% more than GPCC over northern Asia and Eurasia. The GRACE-based estimate (49% more than GPCC) was the closest to GPCC-F. We found that as near-surface air temperature decreased, the products increasingly underestimated the GRACE-based snowfall accumulation. Overall, GRACE showed that CFs are effective in improving GPCC estimates. Furthermore, our case studies and overall statistics suggest that CF-F is likely more effective than CF-L in most of the high-latitude regions studied here. GPCP showed generally better skill than GPCC-L, which might be related to the use of satellite data or additional quality controls on gauge inputs to GPCP. This study suggests that GPCP can be improved if it employs CF-L instead of CF-F to correct for gauge undercatch. However, this implementation requires further studies, region-specific analysis, and operational considerations. [ABSTRACT FROM AUTHOR]

The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%. [ABSTRACT FROM AUTHOR]

The Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA), Edition 4.0 (Ed4.0), data product is described. EBAF Ed4.0 is an update to EBAF Ed2.8, incorporating all of the Ed4.0 suite of CERES data product algorithm improvements and consistent input datasets throughout the record. A one-time adjustment to shortwave (SW) and longwave (LW) TOA fluxes is made to ensure that global mean net TOA flux for July 2005-June 2015 is consistent with the in situ value of 0.71 W m-². While global mean all-sky TOA flux differences between Ed4.0 and Ed2.8 are within 0.5 W m-², appreciable SW regional differences occur over marine stratocumulus and snow/sea ice regions. Marked regional differences in SW clear-sky TOA flux occur in polar regions and dust areas over ocean. Clear-sky LW TOA fluxes in EBAF Ed4.0 exceed Ed2.8 in regions of persistent high cloud cover. Owing to substantial differences in global mean clear-sky TOA fluxes, the net cloud radiative effect in EBAF Ed4.0 is -18 W m-² compared to -21 W m-² in EBAF Ed2.8. The overall uncertainty in 1° x 1° latitude-longitude regional monthly all-sky TOA flux is estimated to be 3 W m-² [one standard deviation (1σ)] for the Terra-only period and 2.5 W m-² for the Terra-Aqua period both for SW and LW fluxes. The SW clear-sky regional monthly flux uncertainty is estimated to be 6 W m-² for the Terra-only period and 5 W m-² for the Terra-Aqua period. The LW clear-sky regional monthly flux uncertainty is 5 W m-² for Terra only and 4.5 W m-² for Terra-Aqua. [ABSTRACT FROM AUTHOR]

Diagnostics of finite-amplitude local wave activity (LWA) are applied to the 500-hPa geopotential height field to diagnose persistent synoptic weather events of anomalously large wave activity in the Northern Hemisphere. By considering the cyclonic and anticyclonic components of LWA separately, persistent weather systems associated with large-amplitude troughs and ridges are detected. While anticyclonic wave events are predominantly found over Europe and Alaska, cyclonic wave events usually occur over East Asia and northeastern Canada. Those preferred regions correspond to the location of planetary-scale ridges and troughs, which contribute, together with transient anomalies, to the formation of wave events. Although wave events are not blocking events per definition, they are typically associated with increased blocking in their vicinity. Their spatial relationship to blocking, however, varies depending on their cyclonic or anticyclonic nature and the type of wave-breaking signatures. Wave events are also shown to be accompanied by warm or cold temperature extremes, whose spatial pattern depends on the type of events, cyclonic or anticyclonic, and the sector affected. Trends in the frequency of wave events indicate that cyclonic wave events and the associated cold extremes affecting East Asia have become more frequent in recent decades and could be linked to recent trends toward La Niña-like conditions in the Pacific and trends toward the negative phase of Arctic Oscillation. [ABSTRACT FROM AUTHOR]

Univariate bias correction algorithms, such as quantile mapping, are used to address systematic biases in climate model output. Intervariable dependence structure (e.g., between different quantities like temperature and precipitation or between sites) is typically ignored, which can have an impact on subsequent calculations that depend on multiple climate variables. A novel multivariate bias correction (MBC) algorithm is introduced as a multidimensional analog of univariate quantile mapping. Two variants are presented. MBCp and MBCr respectively correct Pearson correlation and Spearman rank correlation dependence structure, with marginal distributions in both constrained to match observed distributions via quantile mapping. MBC is demonstrated on two case studies: 1) bivariate bias correction of monthly temperature and precipitation output from a large ensemble of climate models and 2) multivariate correction of vertical humidity and wind profiles, including subsequent calculation of vertically integrated water vapor transport and detection of atmospheric rivers. The energy distance is recommended as an omnibus measure of performance for model selection. As expected, substantial improvements in performance relative to quantile mapping are found in each case. For reference, characteristics of the MBC algorithm are compared against existing bivariate and multivariate bias correction techniques. MBC performs competitively and fills a role as a flexible, general purpose multivariate bias correction algorithm. [ABSTRACT FROM AUTHOR]

The Pacific-South American (PSA) pattern is an important mode of climate variability in the mid-to-high southern latitudes. It is widely recognized as the primary mechanism by which El Niño-Southern Oscillation (ENSO) influences the southeast Pacific and southwest Atlantic and in recent years has also been suggested as a mechanism by which longer-term tropical sea surface temperature trends can influence the Antarctic climate. This study presents a novel methodology for objectively identifying the PSA pattern. By rotating the global coordinate system such that the equator (a great circle) traces the approximate path of the pattern, the identification algorithm utilizes Fourier analysis as opposed to a traditional empirical orthogonal function approach. The climatology arising from the application of this method to ERA-Interim reanalysis data reveals that the PSA pattern has a strong influence on temperature and precipitation variability over West Antarctica and the Antarctic Peninsula and on sea ice variability in the adjacent Amundsen, Bellingshausen, and Weddell Seas. Identified seasonal trends toward the negative phase of the PSA pattern are consistent with warming observed over the Antarctic Peninsula during autumn, but are inconsistent with observed winter warming over West Antarctica. Only a weak relationship is identified between the PSA pattern and ENSO, which suggests that the pattern might be better conceptualized as a preferred regional atmospheric response to various external (and internal) forcings. [ABSTRACT FROM AUTHOR]

Ensembles of climate models are commonly used to improve decadal climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, an ensemble of decadal climate predictions is used to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression, and the climatology. Predictions of four different variables-the surface temperature, the zonal and meridional wind, and pressure-are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. The reliability of the SLAs is also tested, and the advantages and limitations of the different measures of the performance are discussed. It was found that the best performances of the SLAs are achieved when the learning period is comparable to the prediction period. The spatial distribution of the SLAs performance showed that they are skillful and better than the other forecasting methods over large continuous regions. This finding suggests that, despite the fact that each of the ensemble models is not skillful, they were able to capture some physical processes that resulted in deviations from the climatology and that the SLAs enabled the extraction of this additional information. [ABSTRACT FROM AUTHOR]

This study formulates the design of optimal observing networks for past surface climate conditions as the solution to a data assimilation problem, given a realistic proxy system model (PSM), paleoclimate observational uncertainties, and a network of current and proposed observing sites. The method is illustrated with the design of optimal networks of coral δ18O records, chosen among candidate sites, and used to jointly infer sea surface temperature (SST) and sea surface salinity (SSS) fields from the Community Climate System Model, version 4, last millennium simulation over the 1850-2005 period. It is shown that an existing paleo-observing network accounts for approximately 20% of the SST variance, and that adding 25 to 100 optimal pseudocoral sites would boost this fraction to 35%-52%. Characterizing the SST variance alone, or jointly with the SSS, leads to similar optimal networks, which justifies using coral δ18O records for SST reconstructions. In contrast, the network design for reconstructing SSS alone is fundamentally different, emphasizing the hydroclimatic centers of action of El Niño-Southern Oscillation. In all cases, network design depends strongly on the amplitude of the observational error, so replicates may be more beneficial than the exploration of new sites; these replicates tend to be chosen where proxies are already informative of the large-scale climate field(s). Finally, extensions to other types of paleoclimatic observations are discussed, and a path to operationalization is outlined. [ABSTRACT FROM AUTHOR]

An objective climatology of anticyclones over the greater Mediterranean region is presented based on the Interim ECMWF Re-Analysis (ERA-Interim) for a 34-yr period (1979-2012) and the Melbourne University automatic identification and tracking algorithm. The scheme's robustness and reliability for the transient extratropical propagation of anticyclones, with the appropriate choices of parameter settings, has been established and the results obtained here present new research perspectives on anticyclonic activity affecting the Mediterranean. Properties of Mediterranean anticyclones, such as frequency, generation and dissipation, movement, scale, and depth are investigated. The highest frequency of anticyclones is found over continental areas, while the highest maritime frequency occurs over closed basins exhibiting also maxima of anticyclogenesis. There is a significant seasonality in system density and anticyclogenesis maxima, this being associated with the seasonal variations of the larger-scale atmospheric circulation that affect the greater Mediterranean region. [ABSTRACT FROM AUTHOR]

A long data record (14 yr) of ground-based observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site is analyzed to document the macroscopic and dynamical properties of daytime fair-weather cumulus clouds during summer months. First, a fuzzy logic-based algorithm is developed to eliminate insect radar echoes in the boundary layer that hinder the ability to develop representative cloud statistics. The refined dataset is used to document the daytime composites of fair-weather cumulus clouds properties. Doppler velocities are processed for lower reflectivity thresholds that contain small cloud droplets having insignificant terminal velocities; thus, Doppler velocities are used as tracers of air motion. The algorithm is implemented to process the entire 14-yr dataset of cloud radar vertical velocity data. Composite diurnal variations of the cloud vertical velocity statistics, surface parameters, and profiles of updraft and downdraft fractions, bulk velocity of updrafts and downdrafts, and updraft and downdraft mass flux are calculated. Statistics on the cloud geometrical properties such as cloud thickness, cloud chord length, cloud spacing, and aspect ratios are calculated on the cloud scale. The present dataset provides a unique insight into the daytime evolution and statistical description of the turbulent structure inside fair-weather cumuli over land. [ABSTRACT FROM AUTHOR]

Perturbed physics configurations of version 3 of the Hadley Centre Atmosphere Model (HadAM3) driven with observed sea surface temperatures (SST) and sea ice were tuned to outgoing radiation observations using a Gauss-Newton line search optimization algorithm to adjust the model parameters. Four key parameters that previous research found affected climate sensitivity were adjusted to several different target values including two sets of observations. The observations used were the global average reflected shortwave radiation (RSR) and outgoing longwave radiation (OLR) from the Clouds and the Earth's Radiant Energy System instruments combined with observations of ocean heat content. Using the same method, configurations were also generated that were consistent with the earlier Earth Radiation Budget Experiment results. Many, though not all, tuning experiments were successful, with about 2500 configurations being generated and the changes in simulated outgoing radiation largely due to changes in clouds. Clear-sky radiation changes were small, largely due to a cancellation between changes in upper-tropospheric relative humidity and temperature. Changes in other climate variables are strongly related to changes in OLR and RSR particularly on large scales. There appears to be some equifinality with different parameter configurations producing OLR and RSR values close to observed values. These models have small differences in their climatology with the one group being similar to the standard configuration and the other group drier in the tropics and warmer everywhere. [ABSTRACT FROM AUTHOR]

The first global objective climatology of tropical plumes (TPs), obtained from a novel algorithm based on gridded 10.8-μm brightness temperatures Tb, is presented for 1983-2006. TPs are defined as continuous cloud bands (>2000 km) crossing 15°N or 15°S with Tb anomalies of less than −20 K and a lifetime of at least 3 h. A minimum length-to-width ratio of 3 filters out elongated features. Numbers of identified TPs are sensitive to the chosen thresholds but not their geographical distribution and seasonal cycle. TPs are an important indicator of tropical-extratropical interactions with impacts on radiation and moisture. TP occurrence during boreal winter is largely confined to oceanic regions with main maxima over the South Pacific and South Atlantic as well as the eastern North Atlantic and Pacific Oceans. The geographical distribution during boreal summer is similar, but with lower frequencies, except for monsoon-influenced regions. Interannual variations over the Indo-Pacific region are strongly related to El Niño. TPs often develop downstream of extratropical upper-level troughs propagating into low latitudes, particularly over the wintertime eastern North Pacific and North Atlantic, but also in regions where mean upper-level easterlies do not generally favor equatorward Rossby wave propagation. Synoptic-scale variations in the quasi-permanent cloud bands associated with the South Pacific and South Atlantic convergence zones frequently produce TP-like anomalies, which are climatologically associated with downstream upper-level troughs. Some regions also feature TPs associated with mesoscale tropical disturbances. The new TP algorithm will serve as a basis for more in-depth studies in the future. [ABSTRACT FROM AUTHOR]

A recently developed object identification algorithm is applied to multisensor precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM 3B42) to detect and quantify the contribution of tropical cyclone precipitation (TCP) to total precipitation between 1998 and 2008. The study period includes 1144 storms. Estimates of TCP derived here are similar in pattern and seasonal variation to earlier estimates but are somewhat higher in magnitude. Annual-mean TCP fractions of over 20% are diagnosed over large swaths of tropical ocean, with seasonal means in some regions of more than 50%. Interannual variability of TCP is examined, and a small but significant downward trend in global TCP from 1998 to 2008 is found, consistent with results from independent studies examining accumulated cyclone energy (ACE). Relationships between annual-mean ACE and TCP in each major tropical cyclone basin are examined. High correlations are found in almost every basin, although different linear relationships exist in each. The highest ACE/TCP ratios are obtained in the North Atlantic and northeast Pacific basins, with lower ratios present in the northwest Pacific and South Pacific basins. [ABSTRACT FROM AUTHOR]

This study introduces a nonlinear clustering technique based on a self-organizing map (SOM) algorithm to identify horizontal and vertical structures of the Madden-Julian oscillation (MJO) through its life cycle. The SOM description of the MJO does not need intraseasonal bandpass filtering or selection of leading modes. MJO phases are defined by SOM based on state similarities in chosen variables. Spatial patterns of rainfall-related variables in a given MJO phase defined by SOM are distinct from those in other phases. The structural evolution of the MJO derived from SOM agrees with those from other methods in certain aspects and differs in others. SOM reveals that the dominant longitudinal structure in the diabatic heating and related fields of the MJO is a dipole or tripole pattern with a zonal scale close to that of zonal wavenumber 2, as opposed to zonal wavenumber 1 suggested by other methods. Results from SOM suggest that the MJO life cycle may be composed of quasi-stationary stages of strong, coherent spatial patterns with relatively fast transition in between that is less coherent and weaker. The utility of SOM to isolate signals of an individual MJO event in a case study is illustrated. The results from this study show that some known gross features of the MJO are independent of diagnostic methods, but other properties of the MJO may be sensitive to the choice of diagnosis method. [ABSTRACT FROM AUTHOR]

The fraction of absorbed photosynthetically active radiation (fAPAR) is an essential diagnostic variable to investigate the temporal and spatial dynamics of the terrestrial biosphere. The present study provides a new method to assess global vegetation greening phase dynamics, derived from fAPAR time series from four different remote sensing products. A robust algorithm is developed to detect intra-annual greening phase patterns and derive seasonality patterns of vegetation dynamics at the global scale. The comparison of four independent remote sensing datasets shows significantly consistent global spatiotemporal patterns at the 95% confidence level. Regions where the remote sensing datasets show consistent results, as well as regions where at least one of the used remote sensing datasets deviates, can be identified. The derived global greening phase dataset and analysis method provides a solid framework for the evaluation of global vegetation models. [ABSTRACT FROM AUTHOR]

Long time series of Arctic atmospheric measurements are assembled into meteorological categories that can serve as test cases for climate model evaluation. The meteorological categories are established by applying an objective k-means clustering algorithm to 11 years of standard surface-meteorological observations collected from 1 January 2000 to 31 December 2010 at the North Slope of Alaska (NSA) site of the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM). Four meteorological categories emerge. These meteorological categories constitute the first classification by meteorological regime of a long time series of Arctic meteorological conditions. The synoptic-scale patterns associated with each category, which include well-known synoptic features such as the Aleutian low and Beaufort Sea high, are used to explain the conditions at the NSA site. Cloud properties, which are not used as inputs to the k-means clustering, are found to differ significantly between the regimes and are also well explained by the synoptic-scale influences in each regime. Since the data available at the ARM NSA site include a wealth of cloud observations, this classification is well suited for model-observation comparison studies. Each category comprises an ensemble of test cases covering a representative range in variables describing atmospheric structure, moisture content, and cloud properties. This classification is offered as a complement to standard case-study evaluation of climate model parameterizations, in which models are compared against limited realizations of the Earth-atmosphere system (e.g., from detailed aircraft measurements). [ABSTRACT FROM AUTHOR]

A linear trend calculated for observed annual mean surface air temperatures over the United States for the second-half of the twentieth century shows a slight cooling over the southeastern part of the country, the so-called warming hole, while temperatures over the rest of the country rose significantly. This east-west gradient of average temperature change has contributed to the observed pattern of changes of record temperatures as given by the ratio of daily record high temperatures to record low temperatures with a comparable east-west gradient. Ensemble averages of twentieth-century climate simulations in the Community Climate System Model, version 3 (CCSM3), show a slight west-east warming gradient but no warming hole. A warming hole appears in only several ensemble members in the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset and in one ensemble member of simulated twentieth-century climate in CCSM3. In this model the warming hole is produced mostly from internal decadal time-scale variability originating mainly from the equatorial central Pacific associated with the Interdecadal Pacific Oscillation (IPO). Analyses of a long control run of the coupled model, and specified convective heating anomaly experiments in the atmosphere-only version of the model, trace the forcing of the warming hole to positive convective heating anomalies in the central equatorial Pacific Ocean near the date line. Cold-air advection into the southeastern United States in winter, and low-level moisture convergence in that region in summer, contribute most to the warming hole in those seasons. Projections show a disappearance of the warming hole, but ongoing greater surface temperature increases in the western United States compared to the eastern United States. [ABSTRACT FROM AUTHOR]

An information-theoretic framework is developed to assess the long-range coarse-grained predictive skill in a perfect-model environment. Central to the scheme is the notion that long-range forecasting involves regimes; specifically, that the appropriate initial data for ensemble prediction is the affiliation of the system to a coarse-grained partition of phase space representing regimes. The corresponding ensemble prediction probabilities, which are computable using ergodic signals from the model, are then used to quantify through relative entropy the information beyond climatology in the partition. As an application, the authors study the predictability of circulation regimes in an equivalent barotropic double-gyre ocean model using a partition algorithm based on K-means clustering and running-average coarse graining. Besides the established rolled up and extensional phases of the eastward jet, optimal partitions for triennial-scale forecasts feature a jet configuration dominated by the second empirical orthogonal function (EOF) of the streamfunction, as well as phases in which the jet interacts with eddies in higher EOFs. Due to mixing dynamics, the skill beyond three-state models is lost for forecast lead times longer than three years, but significant skill remains in the energy and the leading principal component of the streamfunction for septennial forecasts. [ABSTRACT FROM AUTHOR]

A detailed analysis is presented of a recently published Antarctic temperature reconstruction that combines satellite and ground information using a regularized expectation--maximization algorithm. Though the general reconstruction concept has merit, it is susceptible to spurious results for both temperature trends and patterns. The deficiencies include the following: (i) improper calibration of satellite data; (ii) improper determination of spatial structure during infilling; and (iii) suboptimal determination of regularization parameters, particularly with respect to satellite principal component retention. This study proposes two methods to resolve these issues. One utilizes temporal relationships between the satellite and ground data; the other combines ground data with only the spatial component of the satellite data. Both improved methods yield similar results that disagree with the previous method in several aspects. Rather than finding warming concentrated in West Antarctica, the authors find warming over the period of 1957--2006 to be concentrated in the peninsula (≈≈0.35°°C decade−−1). This study also shows average trends for the continent, East Antarctica, and West Antarctica that are half or less than that found using the unimproved method. Notably, though the authors find warming in West Antarctica to be smaller in magnitude and find that statistically significant warming extends at least as far as Marie Byrd Land. This study also finds differences in the seasonal patterns of temperature change, with winter and fall showing the largest differences and spring and summer showing negligible differences outside of the peninsula. [ABSTRACT FROM AUTHOR]

Mesoscale cyclones play an important role in the weather and climate of the Southern Hemisphere (SH) mid-to-high latitudes. However, the relatively small size and short lifetime of these systems, combined with the lack of available conventional data in this region, means that there is a poor understanding of their climatological characteristics. In this study, the University of Melbourne cyclone-finding algorithm was applied to relatively high-resolution scatterometer-derived surface pressure fields, obtained from the Department of Atmospheric Sciences at the University of Washington, to produce a decade-long (1999--2008) seasonal climatology of mesoscale cyclone activity over the ice-free regions of the Southern Ocean. The frequency of mesoscale cyclone activity was found to be highest just to the north of the sea ice zone, reaching a maximum over the Amundsen and Bellingshausen Seas (ABS), while the southern Indian Ocean was associated with systems of the largest depth, intensity, and momentum flux at the ocean surface. These spatial patterns in mean mesoscale cyclone characteristics showed a broad resemblance to those reported in existing synoptic-scale cyclone climatologies. Maximum wind speed data indicated that SH polar lows may be more frequent than the current literature suggests, while strong positive trends identified in mesoscale cyclone frequency over the ABS may represent a contributing factor to the rapid warming observed in that region over recent years. Partial correlation analyses indicated a link between mesoscale cyclone frequency and the southern annular mode. [ABSTRACT FROM AUTHOR]

Part I presented a Bayesian algorithm for reconstructing climate anomalies in space and time (BARCAST). This method involves specifying simple parametric forms for the spatial covariance and temporal evolution of the climate field as well as “observation equations” describing the relationships between the data types and the corresponding true values of the climate field. As this Bayesian approach to reconstructing climate fields is new and different, it is worthwhile to compare it in detail to the more established regularized expectation–maximization (RegEM) algorithm, which is based on an empirical estimate of the joint data covariance matrix and a multivariate regression of the instrumental time series onto the proxy time series. The differing assumptions made by BARCAST and RegEM are detailed, and the impacts of these differences on the analysis are discussed. Key distinctions between BARCAST and RegEM include their treatment of spatial and temporal covariance, the prior information that enters into each analysis, the quantities they seek to impute, the end product of each analysis, the temporal variance of the reconstructed field, and the treatment of uncertainty in both the imputed values and functions of these imputations. Differences between BARCAST and RegEM are illustrated by applying the two approaches to various surrogate datasets. If the assumptions inherent to BARCAST are not strongly violated, then in scenarios comparable to practical applications BARCAST results in reconstructions of both the field and the spatial mean that are more skillful than those produced by RegEM, as measured by the coefficient of efficiency. In addition, the uncertainty intervals produced by BARCAST are narrower than those estimated using RegEM and contain the true values with higher probability. [ABSTRACT FROM AUTHOR]

The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate–high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.5° horizontal resolution, the occurrence of conditional rain rates over 100 mm day−1 is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations. An improved convective–stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm. [ABSTRACT FROM AUTHOR]

A hierarchical Bayesian framework is developed to identify multiple abrupt regime shifts in an extreme event series. Specifically, extreme events are modeled as a Poisson process with a gamma-distributed rate. Multiple candidate hypotheses are considered, under each of which there presumably exist a certain number of abrupt shifts of the rate. A Bayesian network involving three layers—data, parameter, and hypothesis—is formulated. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is developed to calculate posterior probability for each hypothesis as well its associated within-hypothesis parameters. Based on the proposed RJMCMC algorithm, a simulated example is designed to illustrate the effectiveness of the method. Subsequently, the algorithm is applied to three real, rare event time series: the annual typhoon counts over the western North Pacific (WNP), the annual extreme heavy rainfall event counts at the Honolulu airport, and the annual heat wave frequency in the Chicago area. Results indicate that the typhoon activity over the WNP is very likely to have undergone a decadal variation, with two change points occurring around 1972 and 1989 characterized by the active 1960–71 epoch, the inactive 1972–88 epoch, and the moderately active 1989–2006 epoch. For the extreme rainfall case, only one shift around 1970 is found and heavy rainfall frequency has remained stationary since then. There is no evidence that the rate of the annual heat wave counts in the Chicago area has had any abrupt change during the past 50 years. [ABSTRACT FROM AUTHOR]