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In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [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]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

ISCCP continues to quantify the global distribution and diurnal-to-interannual variations of cloud properties in a revised version. This paper summarizes assessments of the previous version, describes refinements of the analysis and enhanced features of the product design, discusses the few notable changes in the results, and illustrates the long-term variations of global mean cloud properties and differing high cloud changes associated with ENSO. The new product design includes a global, pixel-level product on a 0.1° grid, all other gridded products at 1.0°-equivalent equal area, separate satellite products with ancillary data for regional studies, more detailed, embedded quality information, and all gridded products in netCDF format. All the data products including all input data, expanded documentation, the processing code, and an operations guide are available online. Notable changes are 1) a lowered ice–liquid temperature threshold, 2) a treatment of the radiative effects of aerosols and surface temperature inversions, 3) refined specification of the assumed cloud microphysics, and 4) interpolation of the main daytime cloud information overnight. The changes very slightly increase the global monthly mean cloud amount with a little more high cloud and a little less middle and low cloud. Over the whole period, total cloud amount slowly decreases caused by decreases in cumulus/altocumulus; consequently, average cloud-top temperature and optical thickness have increased. The diurnal and seasonal cloud variations are very similar to earlier versions. Analysis of the whole record shows that high cloud variations, but not low clouds, exhibit different patterns in different ENSO events. Significance Statement: This paper reports on the evolution of the research goals and satellite cloud data products produced by ISCCP, a long-term international project, which is now fully operational. The growing length of record with 10-km and 3-h sampling makes possible studies of cloud variations from diurnal-to-weather scale (cloud process scale) to climate variation scale. In particular the length of record includes many examples of ENSO and is beginning to encompass the slower modes of ocean variation, allowing studies of the role of cloud feedbacks in coupling the atmosphere and ocean circulations. [ABSTRACT FROM AUTHOR]

Although tropical interbasin interactions at interannual time scales are presently receiving much attention, their controlling factors and variations on longer time scales are under debate. Tropical convection plays a crucial role in the occurrence and nonstationarity of them. In this paper, we investigate the dependence of interannual tropical Atlantic–Pacific basin interactions on convection-related features of the tropical oceans' climatology, especially the ITCZ position. We contrast a CGCM control simulation with an experiment in which tropical convection is modified by an artificial perturbation outside the tropics that reduces the incident shortwave radiation in a region of the South Atlantic. Based on previous work, this modification is expected to shift in latitude the climatological position of the simulated ITCZ. The experiment shows altered Walker circulations, stronger interannual variability over the tropical oceans, a westward extension of the Atlantic Niño pattern and of convection, and shallower thermocline in the Pacific, making the basin more sensitive to both local and remote perturbations. As a consequence, the experiment shows enhanced interannual Atlantic–Pacific basin interactions at the equator, and weaker teleconnections between the north tropical Atlantic and the equatorial Pacific. The latter seems to occur because the impact of the warm Atlantic SST anomalies is offset by the presence of warm SST anomalies in El Niño region. Despite the uncertainties raised because the simulations are relatively short, we conclude that this work presents a potential explanation for the long-term changes in the tropical basin interactions and offers a novel and useful methodology for their analysis. [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]

Cape Farewell, Greenland's southernmost point, is characterized by a number of low-level jets that are the result of topographic flow distortion associated with passing extratropical cyclones. The heavy seas associated with these wind events are a hazard to maritime traffic in the region. In addition, the air-sea heat flux associated with these weather systems plays an important role in the climate system by contributing to the forcing of the lower limb of the Atlantic meridional overturning circulation. In this paper, the North American Regional Reanalysis will be used to generate a higher-resolution climatology of these mesoscale jets as compared to previous studies. Through the use of a diagnostic that partitions the occurrence frequency of high-speed wind events by wind direction, the author shows that there are four different types of Cape Farewell tips jets that are characterized as having either northwesterly, southwesterly, northeasterly, or southeasterly wind direction. All four types have distinct regions in the vicinity of Cape Farewell where their respective occurrence frequencies and air-sea heat fluxes are at a maximum. The southwesterly and northeasterly jets closely resemble the wind systems previously identified as being westerly and easterly tip jets. There are also instances where one type evolves into another and so it is possible to view westerly tip jets as a continuum with the northwesterly and southwesterly events identified in this paper representing end members with a similar picture for easterly tip jets. The position of a particular event along these continua will determine its impact on local weather and the coupled climate system. [ABSTRACT FROM AUTHOR]

A recent paper by Kato and Rose reports a negative correlation between the annual mean entropy production rate of the climate and the absorption of solar radiation in the CERES SYN1deg dataset, using the simplifying assumption that the system is steady in time. It is shown here, however, that when the nonsteady interannual storage of entropy is accounted for, the dataset instead implies a positive correlation; that is, global entropy production rates increase with solar absorption. Furthermore, this increase is consistent with the response demonstrated by an energy balance model and a radiative–convective model. To motivate this updated analysis, a detailed discussion of the conceptual relationship between entropy production, entropy storage, and entropy flows is provided. The storage-corrected estimate for the mean global rate of entropy production in the CERES dataset from all irreversible transfer processes is 81.9 mW m−2 K−1 and from only nonradiative processes is 55.2 mW m−2 K−1 (observations from March 2000 to February 2018). [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]

Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

This paper describes the development and analysis of an objective climatology of warm and cold fronts over North America from 1979 to 2018. Fronts are detected by a convolutional neural network (CNN), trained to emulate fronts drawn by human meteorologists. Predictors for the CNN are surface and 850-hPa fields of temperature, specific humidity, and vector wind from the ERA5 reanalysis. Gridded probabilities from the CNN are converted to 2D frontal regions, which are used to create the climatology. Overall, warm and cold fronts are most common in the Pacific and Atlantic cyclone tracks and the lee of the Rockies. In contrast with prior research, we find that the activity of warm and cold fronts is significantly modulated by the phase and intensity of El Niño–Southern Oscillation. The influence of El Niño is significant for winter warm fronts, winter cold fronts, and spring cold fronts, with activity decreasing over the continental United States and shifting northward with the Pacific and Atlantic cyclone tracks. Long-term trends are generally not significant, although we find a poleward shift in frontal activity during the winter and spring, consistent with prior research. We also identify a number of regional patterns, such as a significant long-term increase in warm fronts in the eastern tropical Pacific Ocean, which are characterized almost entirely by moisture gradients rather than temperature gradients. [ABSTRACT FROM AUTHOR]

This paper addresses the dynamics of internal hemispheric-scale multidecadal climate variability by postulating an energy-balance (EBM) model comprising two deep-ocean oscillators in the Atlantic and Pacific basins, coupled through their surface mixed layers via atmospheric teleconnections. This system is linear and driven by the atmospheric noise. Two sets of the EBM model parameters are developed by fitting the EBM-based mixed-layer temperature covariance structure to best mimic basin-average North Atlantic/Pacific sea surface temperature (SST) covariability in either observations or control simulations of comprehensive climate models within the CMIP5 project. The differences between the dynamics underlying the observed and CMIP5-simulated multidecadal climate variability and predictability are encapsulated in the algebraic structure of the two EBM model versions so obtained: EBMCMIP5 and EBMOBS. The multidecadal variability in EBMCMIP5 is overall weaker and amounts to a smaller fraction of the total SST variability than in EBMOBS, pointing to a lower potential decadal predictability of virtual CMIP5 climates relative to that of the actual climate. The EBMCMIP5 decadal hemispheric teleconnections (and, by inference, those in CMIP5 models) are largely controlled by the variability of the Pacific, in which the ocean, due to its large thermal and dynamical memory, acts as a passive integrator of atmospheric noise. By contrast, EBMOBS features a stronger two-way coupling between the Atlantic and Pacific multidecadal oscillators, thereby suggesting the existence of a hemispheric-scale and, perhaps, global multidecadal mode associated with internal ocean dynamics. The inferred differences between the observed and CMIP5 simulated climate variability stem from a stronger communication between the deep ocean and surface processes implicit in the observational data. [ABSTRACT FROM AUTHOR]

As the most extensive highland in the world, the Tibetan Plateau (TP) plays an important role in shaping the global climate. Quantifying the effect of the TP on global climate is the first step for a full understanding of the TP's standing on planet Earth. Through coupled model sensitivity experiments, we draw a panorama of the TP's global impact in this paper. Our model results show that the absence of the TP would result in a 4°C colder and 10% drier climate in the Northern Hemisphere (NH). The TP has a striking remote effect on the North Atlantic. Removing the TP would enhance the westerlies in the mid- to high latitudes of the NH and weaken the easterlies over the tropical Pacific. More moisture would be relocated from the tropical Pacific to the North Atlantic, shutting down the Atlantic thermohaline circulation, which would eventually result in more than 15°C colder and 20% drier climate over the North Atlantic. Our model results suggest that the presence of the TP may have contributed greatly to the hospitable modern climate in the NH, by promoting the establishment of the thermohaline circulation in the Atlantic, and therefore enhancing the northward ocean heat transport and atmosphere moisture transport across the equator. [ABSTRACT FROM AUTHOR]

Climate classification aims to divide a given region into distinct groups on the basis of meteorological variables, and it has important applications in fields such as agriculture and buildings. In this paper, we propose a novel spectral clustering–based method to partition 661 meteorological stations in China into several zones. First, the correlations are analyzed among five meteorological variables: daily average temperature, average relative humidity, sunshine hours, diurnal temperature range, and atmospheric pressure. The resulting weak linear correlation supports the classification under multiple views. Next, a similarity matrix/graph is constructed by combining the advantages of k-nearest-neighbor and sparse subspace representation. The blocking effect of the similarity matrix confirms the rationality of the classification. Then, we consider respectively the climate classification under a single view and multiple views. For the single view, atmospheric pressure has the highest imbalance degree, and sunshine hours and diurnal temperature range have the strongest classification consistency. The consistency rates are improved evidently in a multiple-view situation. Afterward, we propose a determining method for the number of clusters and make a sensitivity analysis on various parameters. Finally, we provide a further statistical analysis on the classification results and compare the consistency with another climate dataset. All experimental results show that the proposed classification method is feasible and effective in the climate zoning of China. [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]

Present-day OGCMs give low values of annual mean net heat flux (AMNHF) in the tropical Indian Ocean, compared to climatologies. AMNHF generation is examined in an open-boundary model of this region with realistic coastlines. In the first two of three experiments only annual mean wind stresses were applied so that a modified form of the “minimum depth criterion” of the previous paper would be applicable. Area-integrated AMNHF was well below observed values, despite the fact that western boundary inflow was substantially deeper and colder than was expected from the modified minimum depth estimate. The model showed large “spikes” in the gradient of “depth-integrated steric height” (DISH) along the western boundary, coinciding with coastline steps (which were absent in the previous paper). Most diapycnal entrainment occurred next to the coast, near these steps. In a third experiment a seasonal cycle of wind stress was added to the same annual mean. Annual mean diapycnal mixing and entrainment increased and the western boundary inflow deepened, resulting in substantially greater AMNHF for the same annual mean Ekman transports. However, area-integrated AMNHF was still well below the mean of directly observed surface fluxes. The recirculation around the “Great Whirl” doubled, permitting more cold water crossing the equator in one year to mix with recirculated water generated in a previous year. Entrainment up to the surface thus went by stages, over more than one year. The increased Great Whirl was related to stronger annual mean curls of nonlinear terms in the momentum equation, while the deeper entrainment was caused by stronger annual mean diapycnal mixing. In all experiments, diapycnal mixing was primarily due to the “flux corrected transport” (FCT) advective scheme, which in effect replaces spurious convective overturn by numerical diffusion. More research is needed to solve such problems, but sensitivity of AMNHF in OGCMs to time-varying forcing—due to seasonal, intraseasonal, or baroclinic instability—may offer a new source of climate predictability. [ABSTRACT FROM AUTHOR]

In this paper the progress made in producing predictions of the Normalized Difference Vegetation Index (NDVI) over Kenya in the Greater Horn of Africa (GHA) for the October–December (OND) season is discussed. Several studies have identified a statistically significant relationship between rainfall and NDVI in the region. Predictability of seasonal rainfall by global climate models (GCMs) during the OND season over the GHA has also been established as being among the best in the world. Information was extracted from GCM seasonal prediction output using statistical transformations. The extracted information was then used in the prediction of NDVI. NDVI is a key variable for management of various climate-sensitive problems. For example, it has been shown to have the potential to predict environmental conditions associated with Rift Valley Fever (RVF) viral activity and this is referred to throughout the paper as a motivation for the study. RVF affects humans and livestock and is particularly economically important in the GHA. The establishment of predictability for NDVI in this paper is therefore part of a methodology that could ultimately generate information useful for managing RVF in livestock in the GHA. It has been shown that NDVI can be predicted skillfully over the GHA with a 2–3-month lead time. Such information is crucial for tailoring forecast information to support RVF monitoring and prediction over the region, as well as many other potential applications (e.g., livestock forage estimation). More generally, the Famine Early Warning System (FEWS), a project of the U.S. Agency for International Development (USAID) and the National Aeronautics and Space Administration (NASA) and other specialized technical centers routinely use NDVI images to monitor environmental conditions worldwide. The high predictability for NDVI established in this paper could therefore supplement the routine monitoring of environmental conditions for a wide range of applications. [ABSTRACT FROM AUTHOR]

A new methodology to study the Madden–Julian oscillation (MJO) is introduced. While previous MJO studies typically have involved highly simplified mathematical models or general circulation models, this new approach seeks to reproduce the MJO by using a regional model with prescribed boundary conditions. This paper reports initial control run results for this methodology using the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) for a domain extending from the western Indian Ocean to the date line. The control run boundaries are forced using the NCEP–NCAR reanalysis (NRA) dataset for a 24-month time period. The climatology for the 24-month period is examined to establish the robustness of MM5 for this region. Results indicate good agreement in the mean winds between the model and the forcing dataset. The primary differences are an easterly bias at 850 hPa and altered flow patterns in the Indian monsoon region. Mean outgoing longwave radiation (OLR) results are good for the model interior with larger discrepancies near the western and eastern boundaries. These discrepancies lead to a reversal of the OLR gradient along the equator. Thirty- to seventy-day bandpassed data are examined to determine how MM5 reproduces the MJO. The modeled and comparison 30–70-day zonal wind and OLR data have similar MJO periodicities, exhibit eastward propagation, and possess the observed seasonal character and vertical structure of the MJO. The “Matthews EOF technique” reveals good similarity between the model and observed OLR. Analysis of vertical profiles of 30–70-day zonal wind reveals that lower-tropospheric winds blow in the opposite direction of upper-level winds for both the model and NRA. Vertical profiles of 30–70-day moist static energy exhibit a peak near the top of the boundary layer. Differences between the model-simulated and observed MJO events have a tendency for the OLR to be relatively noisy and for peak OLR intensity to occur in the west Indian Ocean in the model, as opposed to the eastern Indian Ocean in observations. This paper establishes the groundwork for a successive paper wherein the boundary forcings will be modified to examine how this alters the modeled MJO. [ABSTRACT FROM AUTHOR]

Although it is critical to assess the accuracy of attribution studies, the fraction of attributable risk (FAR) cannot be directly assessed from observations since it involves the probability of an event in a world that did not happen, the 'natural' world where there was no human influence on climate. Instead, reliability diagrams (usually used to compare probabilistic forecasts to the observed frequencies of events) have been used to assess climate simulations employed for attribution and by inference to evaluate the attribution study itself. The Brier score summarizes this assessment of a model by the reliability diagram. By constructing a modeling framework where the true FAR is already known, this paper shows that Brier scores are correlated to the accuracy of a climate model ensemble's calculation of FAR, although only weakly. This weakness exists because the diagram does not account for accuracy of simulations of the natural world. This is better represented by two reliability diagrams from early and late in the period of study, which would have, respectively, less and greater anthropogenic climate forcing. Two new methods are therefore proposed for assessing the accuracy of FAR, based on using the earlier observational period as a proxy for observations of the natural world. It is found that errors from model-based estimates of these observable quantities are strongly correlated with errors in the FAR estimated in the model framework. These methods thereby provide new observational estimates of the accuracy in FAR. [ABSTRACT FROM AUTHOR]

Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is the midtropospheric measurements made by the Microwave Sounding Unit (MSU) channel 2 and the Advanced Microwave Sounding Unit (AMSU) channel 5. Previous versions of the Remote Sensing Systems (RSS) dataset have used a diurnal climatology derived from general circulation model output to remove the effects of drifting local measurement time. This paper presents evidence that this previous method is not sufficiently accurate and presents several alternative methods to optimize these adjustments using information from the satellite measurements themselves. These are used to construct a number of candidate climate data records using measurements from 15 MSU and AMSU satellites. The new methods result in improved agreement between measurements made by different satellites at the same time. A method is chosen based on an optimized second harmonic adjustment to produce a new version of the RSS dataset, version 4.0. The new dataset shows substantially increased global-scale warming relative to the previous version of the dataset, particularly after 1998. The new dataset shows more warming than most other midtropospheric data records constructed from the same set of satellites. It is also shown that the new dataset is consistent with long-term changes in total column water vapor over the tropical oceans, lending support to its long-term accuracy. [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]

The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models. [ABSTRACT FROM AUTHOR]

Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources. Climate scenarios generated by climate models at spatial resolutions ranging from about 50 km to 400 km may not provide enough spatial specificity for use in impact assessment. In Parts I and II of this paper, the spatial specificity issue is addressed by examining what information on mesoscale and small-scale spatial features can be gained by using a regional climate model with a subgrid parameterization of orographic precipitation and land surface cover, driven by a general circulation model. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO[sub2] concentration. This paper describes and contrasts the large-scale and mesoscale features of the greenhouse warming climate signals simulated by the general circulation model and regional climate model over the Pacific Northwest. Results indicate that changes in the large-scale circulation exhibit strong seasonal variability. There is an average warming of about 2°C, and precipitation generally increases over the Pacific Northwest and decreases over California. The precipitation signal over the Pacific Northwest is only statistically significant during spring, when both the change in the large-scale circulation and increase in water vapor enhance the moisture convergence toward the north Pacific coast. The combined effects of surface temperature and precipitation changes are such that snow cover is reduced by up to 50% on average, causing large changes in the seasonal runoff. This paper also describes the high spatial resolution (1.5 km) climate signals simulated by the regional climate model. Reductions in snow cover of 50%-90% are found in areas near the snow line of the control simulation. Analyses of the variations of the... [ABSTRACT FROM AUTHOR]

This paper reports a consistent seesaw relationship between interdecadal precipitation variability over North China and the Southwest United States, which can be found in observations and simulations with several models. Idealized model simulations suggest the seesaw could be mainly driven by the interdecadal Pacific oscillation (IPO), through a large-scale circulation anomaly occupying the entire northern North Pacific, while the Atlantic multidecadal oscillation (AMO) contributes oppositely and less. Modulation of precipitation by the IPO tends to be intensified when the AMO is in the opposite phase, but weakened when the AMO is in the same phase. The warm IPO phase is associated with an anomalous cyclone over the northern North Pacific; consequently, anomalous southwesterly winds bring more moisture and rainfall to the Southwest United States, while northwesterly wind anomalies prevail over North China with negative rainfall anomalies. The east–west seesaw of rainfall anomalies reverses sign when the circulation anomaly becomes anticyclonic during the cold IPO phase. The IPO-related tropical SST anomalies affect the meridional temperature gradient over the North Pacific and adjacent regions and the mean meridional circulation. In the northern North Pacific, the atmospheric response to IPO forcing imposes an equivalent barotropic structure throughout the troposphere. An important implication from this study is the potential predictability of drought-related water stresses over these arid and semiarid regions, with the progress of our understanding and prediction of the IPO and AMO. [ABSTRACT FROM AUTHOR]

The Pacific equatorial cold tongue plays a leading role in Earth's strongest and most predictable climate signals. To illuminate the processes governing cold tongue temperatures, the upper-ocean heat budget is explored using the GFDL-FLOR coupled GCM (the forecast-oriented low ocean resolution version of CM2.5). Starting from the exact temperature budget for layers of time-varying thickness, the layer temperature tendency terms are studied using hourly-, daily-, and monthly-mean output from a 30-yr simulation driven by present-day radiative forcings. The budget is then applied to 1) a surface mixed layer whose temperature is highly correlated with SST, in which the air–sea heat flux is balanced mainly by downward diffusion of heat across the layer base, and 2) a thicker advective layer that subsumes most of the vertical mixing, in which the air–sea heat flux is balanced mainly by monthly-scale advection. The surface warming from shortwave fluxes and submonthly meridional advection and the subsurface cooling from monthly vertical advection are both shown to be essential to maintain the cold tongue thermal stratification against the destratifying effects of vertical mixing. Although layer undulations strongly mediate the tendency terms on diurnal-to-interannual scales, the 30-yr-mean tendencies are found to be well summarized by analogous budgets developed for stationary but spatially varying layers. The results are used to derive practical simplifications of the exact budget, to support the analyses in Part II of this paper, and to facilitate broader application of heat budget analyses when evaluating and comparing climate simulations. [ABSTRACT FROM AUTHOR]

This paper presents an object-based, global climatology (1979-2014) of strong large-scale ocean evaporation (SLOE) and its associated climatic properties. SLOE is diagnosed using an ''atmospheric moisture uptake efficiency'' criterion related to the ratio of surface evaporation and integrated water vapor content in the near-surface atmosphere. The chosen Eulerian identification procedure focuses on events that strongly contribute to the available near-surface atmospheric humidity. SLOE is particularly frequent along the warm ocean western boundary currents, downstream of large continental areas, and at the sea ice edge in polar regions with frequent cold-air outbreaks. Furthermore, wind-driven SLOE occurs in regions with topographically enforced winds. On a global annual average, SLOE occurs only 6% of the time but explains 22% of total ocean evaporation. An analysis of the past history and fate of air parcels involved in cold season SLOE in the North Atlantic and south Indian Oceans shows that cold-air advection is the main mechanism that induces these events. Extratropical cyclones thereby play an important role in setting the necessary equatorward synoptic flow. Consequently, the interannual variability of SLOE associated with the North Atlantic Oscillation and the southern annular mode reveals a very high sensitivity of SLOE with respect to the location of the storm tracks. This study highlights the strong link between transient synoptic events and the spatiotemporal variability in ocean evaporation patterns, which cannot be deduced from thermodynamic steadystate and climate mean state considerations alone. [ABSTRACT FROM AUTHOR]

Unlike other cloud types, high-level clouds play an important role, often imposing a warming effect, in the earth-atmosphere radiative energy budget. In this paper, macro- and microphysical characteristics of cirrus clouds, such as their occurrence frequency, geometric scale, water content, and particle size, over northern China (land area, herein called the L area) and the Pacific Ocean (ocean area, herein the O area) are analyzed and compared based on CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) products from 1 January 2007 to 31 December 2010. Over both areas, statistical analysis shows that cirrus occurrence approached 33% in summer whereas it was only ~10% in winter, >50% of cirrus cloud thicknesses were in the range of ~(0.25-1.5) km, there were >98% ice particles in high-level clouds, and temperature had a closer linear relationship with ice effective radius (IER) than height. Also, the seasonal difference of this linear relationship is minor over both land and ocean. Comparisons reveal that the mean occurrence frequency, mean cloud thickness, range of cloud-base and cloud-top height, IER, and ice water content of cirrus in summer were generally greater in winter, and greater over the O area than over the L area. However, the relationship between IER and temperature over land is close to that over ocean. [ABSTRACT FROM AUTHOR]

In this paper, the authors use NCEP reanalysis and 40-yr ECMWF Re-Analysis (ERA-40) data to document the strengthened relationship between the East Asian winter monsoon (EAWM) and winter Arctic Oscillation (AO) on the interannual time scale with a comparison of 1950-70 and 1983-2012. Their connection was statistically insignificant during 1950-70, whereas it was statistically significant during 1983-2012. The latter significant connection might be attributed to the East Asian jet stream (EAJS) upstream extension: the EAJS signal is relatively confined to the western North Pacific before the 1970s, whereas it extends westward toward East Asia after the 1980s. This upstream extension leads to the rearrangement of eastward-propagating Rossby waves with a much wider horizontal structure, thereby bonding the EAWM and the AO. Furthermore, the authors present observational evidence and model simulations demonstrating that the reduction of autumn Arctic sea ice cover (ASIC) is responsible for the strengthened EAWM-AO relationship after the 1980s by producing the EAJS upstream extension. After the 1980s, a strong anticyclonic anomaly over the polar ocean and anomalous easterly advection over northern Eurasia are generated by the near-surface heating over the Barents-Kara (B-K) Seas caused by the reduction of ASIC. This further induces cold anomalies over northern Eurasia, altering the meridional temperature gradient between the midlatitude and tropical region and consequently leading to westward penetration of the EAJS. [ABSTRACT FROM AUTHOR]

This is the first part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the historical simulations of continental and regional climatology with a focus on a core set of 17 models. The authors evaluate the models for a set of basic surface climate and hydrological variables and their extremes for the continent. This is supplemented by evaluations for selected regional climate processes relevant to North American climate, including cool season western Atlantic cyclones, the North American monsoon, the U.S. Great Plains low-level jet, and Arctic sea ice. In general, the multimodel ensemble mean represents the observed spatial patterns of basic climate and hydrological variables but with large variability across models and regions in the magnitude and sign of errors. No single model stands out as being particularly better or worse across all analyses, although some models consistently outperform the others for certain variables across most regions and seasons and higher-resolution models tend to perform better for regional processes. The CMIP5 multimodel ensemble shows a slight improvement relative to CMIP3 models in representing basic climate variables, in terms of the mean and spread, although performance has decreased for some models. Improvements in CMIP5 model performance are noticeable for some regional climate processes analyzed, such as the timing of the North American monsoon. The results of this paper have implications for the robustness of future projections of climate and its associated impacts, which are examined in the third part of the paper. [ABSTRACT FROM AUTHOR]

This paper describes climate simulations of the Community Atmosphere Model, version 5 (CAM5), coupled with a higher-order turbulence closure known as Cloud Layers Unified by Binormals (CLUBB). CLUBB is a unified parameterization of the planetary boundary layer (PBL) and shallow convection that is centered around a trivariate probability density function (PDF) and replaces the conventional PBL, shallow convection, and cloud macrophysics schemes in CAM5. CAM-CLUBB improves many aspects of the base state climate compared to CAM5. Chief among them is the transition of stratocumulus to trade wind cumulus regions in the subtropical oceans. In these regions, CAM-CLUBB provides a much more gradual transition that is in better agreement with observational analysis compared to CAM5, which is too abrupt. The improvement seen in CAM-CLUBB can be largely attributed to the gradual evolution of the simulated turbulence, which is in part a result of the unified nature of the parameterization, and to the general improved representation of shallow cumulus clouds compared to CAM5. In addition, there are large differences in the representation and structure of marine boundary layer clouds between CAM-CLUBB and CAM5. CAM-CLUBB is also shown to be more robust, in terms of boundary layer clouds, to changes in vertical resolution for global simulations in a preliminary test. [ABSTRACT FROM AUTHOR]

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS. [ABSTRACT FROM AUTHOR]

Discrepancies between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) oceanic rainfall retrievals are prevalent between El Niño and La Niña conditions with TMI exhibiting systematic shifts in precipitation. To investigate the causality of this relationship, this paper focuses on the evolution of precipitation organization between El Niño and La Niña and their impacts on TRMMprecipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation duringElNiñowith the largest variability occurring in the Pacific basins. During El Niño, organized systems are more frequent, have increased areal coverage of stratiform rainfall, and penetrate deeper into the troposphere compared to La Niña. The increased stratiform raining fraction leads to larger increases inTMI rain rates than PR rain rate retrievals.Reanalysis and water vapor data from the Atmospheric Infrared Sounder (AIRS) indicate that organized systems are aided by midtropospheric moisture increases accompanied by increased convective frequency. During La Niña, tropical rainfall is dominated by isolated deep convection due to drier midtropospheric conditions and strong mid- and upper-level zonal wind shear. To examine tropical rainfall--sea surface temperature relations, regime-based bias corrections derived using ground validation (GV) measurements are applied to the TRMM rain estimates. The robust connection with GV-derived biases and oceanic precipitation leads to a reduction in TMI-PR regional differences and tropics-wide precipitation anomalies.The improved agreement betweenPRandTMI estimates yields positive responses of precipitation to tropical SSTs of 10% 8C-1 and 17% 8C-1, respectively, consistent with 15% 8C-1 from the Global Precipitation Climatology Project (GPCP). [ABSTRACT FROM AUTHOR]

This paper presents observational evidence of the atmospheric circulation during the Late Maunder Minimum (LMM, 1685-1715) based on daily wind direction observations from ships in the English Channel. Four wind directional indices and 8-point wind roses are derived at monthly scales to characterize the LMM. The results indicate that the LMM was characterized by a pronounced meridional circulation and a marked reduction in the frequency of westerly days all year round, as compared to the present (1981-2010). The winter circulation contributed the most to the cold conditions. Nevertheless, findings indicate that the LMM in Europe was more heterogeneous than previously thought, displaying contrasting spatial patterns in both circulation and temperature, as well as large decadal variability. In particular, there was an increase of northerly winds favoring colder winters in the first half of the LMM, but enhanced southerlies contributing to milder conditions in the second half of the LMM. The analysis of the atmospheric circulation yields a new and complete classification ofLMMwinters. The temperature inferred from the atmospheric circulation confirms the majority of extremely cold winters well documented in the literature, while uncovering other less documented cold and mild winters. The results also suggest a nonstationarity of the North Atlantic Oscillation (NAO) pattern within the LMM, with extremely cold winters being driven by negative phases of a ''high zonal'' NAO pattern and ''low zonal'' NAO patterns dominating during moderately cold winters. [ABSTRACT FROM AUTHOR]

Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations-such as those at airports-also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day's temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax 1 Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax 1 Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001-15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. [ABSTRACT FROM AUTHOR]

The link between weather regimes (WRs) and polar low (PL) activity is examined over the North Atlantic and North Pacific basins. Compared to earlier studies based on limited, regional PL datasets, our study conducts a more complete evaluation of regional WR–PL relationships using an expanded PL climatology. Our findings show that PL activity is increased over the Norwegian and Barents Seas during the Atlantic ridge regime and decreased over the former region during the Scandinavian blocking regime, with negative impacts also stretching to the Irminger Sea. Over the Labrador and Irminger Seas, PL activity is modulated strongest by the North Atlantic Oscillation (NAO), with positive impacts during the positive phase and vice versa. Over the North Pacific, the Arctic low contributes to increased PL activity over most regions, while the opposite is true for the Pacific wave train regime. The variability of PL activity associated with WRs is strongly related to changes in key environmental conditions. In general, regions of enhanced (reduced) PL activity are coincident with anomalous low-level northerly (southerly) flow and reduced (increased) static stability. Further analysis shows that certain persistent WRs can strongly modulate PL activity over some regions, either due to the amplification or propagation of favorable or unfavorable conditions, which cautions the limitation of regarding WRs as stationary patterns. A previously developed PL genesis potential index is shown to represent the observed impacts well, which serves to confirm the robustness of our findings and suggests the potential applicability of WRs to the subseasonal prediction of PLs. Significance Statement: Polar lows are intense small-scale (∼300 km) cyclones that form over high-latitude oceanic regions. The hazardous impacts they pose to coastal communities and maritime and air operations in the Arctic motivate their skillful prediction, which remains a major challenge at lead times beyond a few days. In this study, we relate PLs and the key environmental conditions that favor their development to weather regimes, which are recurrent large-scale circulation patterns that can persist for weeks at a time. We find that weather regimes have strong impacts on polar low activity through the modulation of key environmental conditions. These relationships can potentially be utilized in the extended-range prediction of polar lows. [ABSTRACT FROM AUTHOR]

Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model's performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China. [ABSTRACT FROM AUTHOR]

The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the 'forecast phase,' the forecasts from all of the member models use the downscaling coefficients of the 'training phase.' The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts. [ABSTRACT FROM AUTHOR]

In this paper, the authors address the impact of uncertainty on estimates of transient climate sensitivity (TCS) of the globally averaged surface temperature, including both uncertainty in past forcing and internal variability in the climate record. This study provides a range of probabilistic estimates of the TCS that combine these two sources of uncertainty for various underlying assumptions about the nature of the uncertainty. The authors also provide estimates of how quickly the uncertainty in the TCS may be expected to diminish in the future as additional observations become available. These estimates are made using a nonlinear Kalman filter coupled to a stochastic, global energy balance model, using the filter and observations to constrain the model parameters. This study verifies that model and filter are able to emulate the evolution of a comprehensive, state-of-the-art atmosphere-ocean general circulation model and to accurately predict the TCS of the model, and then apply the methodology to observed temperature and forcing records of the twentieth century. For uncertainty assumptions best supported by global surface temperature data up to the present time, this paper finds a most likely present-day estimate of the transient climate sensitivity to be 1.6 K, with 90%% confidence the response will fall between 1.3 and 2.6 K, and it is estimated that this interval may be 45%% smaller by the year 2030. The authors calculate that emissions levels equivalent to forcing of less than 475 ppmv CO2 concentration are needed to ensure that the transient temperature response will not exceed 2 K with 95%% confidence. This is an assessment for the short-to-medium term and not a recommendation for long-term stabilization forcing; the equilibrium temperature response to this level of CO2 may be much greater. The flat temperature trend of the last decade has a detectable but small influence on TCS. This study describes how the results vary if different uncertainty assumptions are made and shows they are robust to variations in the initial prior probability assumptions. [ABSTRACT FROM AUTHOR]

Results from two perturbation experiments using the Community Climate System Model version 4 where the Southern Hemisphere zonal wind stress is increased are described. It is shown that the ocean response is in accord with experiments using much-higher-resolution ocean models that do not use an eddy parameterization. The key to obtaining an appropriate response in the coarse-resolution climate model is to specify a variable coefficient in the Gent and McWilliams eddy parameterization, rather than a constant value. This result contrasts with several recent papers that have suggested that coarse-resolution climate models cannot obtain an appropriate response. [ABSTRACT FROM AUTHOR]

A full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentieth-century remains elusive some 25 yr after the height of the event. Previous studies have suggested that this drying trend may be explained by either decadal modes of natural variability or by human-driven emissions (primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal variability and model formulation. The sizes of these ensembles enable the relative role of human-driven changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic aerosol changes are likely to explain most of the underlying 1940-80 drying signal and a notable proportion of the more pronounced 1950-80 drying. [ABSTRACT FROM AUTHOR]

This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958--2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5°° ×× 5°° latitude--longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93%% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25%% for 72%% of the global grid cells when compared against the best-performing single forecast among the three climate models considered. [ABSTRACT FROM AUTHOR]

A large fraction of papers in the climate literature includes erroneous uses of significance tests. A Bayesian analysis is presented to highlight the meaning of significance tests and why typical misuse occurs. The significance statistic is not a quantitative measure of how confident one can be of the ''reality'' of a given result. It is concluded that a significance test very rarely provides useful quantitative information. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA), which is designed to look at internal modes of variability, has often been applied beyond its intended design to study coupled modes of variability in combined datasets, also referred to as combined PCA. There are statistical techniques better suited for this purpose such as singular value decomposition (SVD) and canonical correlation analysis (CCA). In this paper, a different technique is examined that has not often been applied in climate science, that is, redundancy analysis (RA). Similar to multivariate regression, RA seeks to maximize the variance accounted for in one random vector that is linearly regressed against another random vector. RA can be used for forecasting and prediction studies of the climate system. This technique has the added advantage that the time-lagged redundancy index offers a robust method of identifying lead-lag relations among climate variables. In this study, combined PCA and RA of global sea surface temperatures (SSTs) and sea level pressures (SLPs) are carried out for the National Centers for Environmental Prediction (NCEP) reanalysis data and a simulation of the Canadian Centre for Climate Modeling and Analysis (CCCma) climate model. A simplified state-space model is also constructed to aid in the diagnosis and interpretation of the results. The relative advantages and disadvantages of combined PCA and RA are discussed. Overall, RA tends to provide a clearer and more consistent picture of the underlying physical processes than combined PCA. [ABSTRACT FROM AUTHOR]

The primary focus of this paper is the analysis of the roles of long-term increases in carbon dioxide (CO2) and sea surface temperatures (used as indicators of climate change) and man-made halocarbons (indicators of chemical ozone depletion linked to halogens) in explaining the observed trend of ozone in the tropical lower stratosphere and implications for related variables including temperature and tropopause height. Published estimates indicate a decrease of approximately 10% in observed ozone concentrations in this region between 1979 and 2005. Using a coupled chemistry–climate atmosphere model forced by observed sea surface temperatures and surface concentrations of long-lived greenhouse gases and halocarbons, the authors show that the simulations display substantial decreases in tropical ozone that compare well in both latitudinal and vertical structure with those observed. Based on sensitivity simulations, the analysis indicates that the decreases in the lower stratospheric (85–50 hPa) tropical ozone distribution are mostly associated with increases in CO2 and sea surface temperatures, in contrast to those at higher latitudes, which are largely driven by halocarbon increases. Factors influencing temperature trends and tropopause heights in this region are also probed. It is shown that the modeled temperature trends in the lower tropical stratosphere are also associated with increases in CO2 and sea surface temperatures. Following the analysis of lower stratospheric tropical temperature trends, the secondary focus of this paper is on related changes in tropopause height. Much of the simulated tropopause rise in the tropical zone as measured by tropopause height is found to be linked to increases in sea surface temperatures and CO2, while increases in halocarbons dominate the tropopause height changes in the subtropics near 30°; both drivers thus affect different regions of the simulated changes in the position of the tropopause. Finally, it is shown that halocarbon increases dominate the changes in the width of the region where modeled total ozone displays tropical character (as indicated by low values of the column abundance). Hence the findings suggest that climate changes and halocarbon changes make different contributions to different metrics used to characterize tropical change. [ABSTRACT FROM AUTHOR]

Recent observations and model studies of the earth’s radiative energy balance have focused attention on the earth’s top of atmosphere (TOA) energy balance. This is the balance between the shortwave energy absorbed by the earth, which is represented by a spatially and temporally averaged absorbed flux , and the emitted longwave energy, which is represented by the corresponding averaged emitted flux . The TOA average net flux FN is defined as the difference between the two over the averaged area and time, which may be a local, regional, or global average. A global nonzero net flux represents a measure of imbalance between the energy being absorbed and emitted by the earth for the time interval in question. It is of interest to ask what the natural variability of the net flux might be and whether, during times of climate change, signals of important climate change processes might be detected against this natural background variation; examples of these signals include evidence of ocean heat storage, the effects of El Niño, and the radiative effects of volcanic eruptions. In this paper, the authors review the significance of the net flux, survey the observational evidence from a range of satellite instruments over several decades, and analyze some of the most recent observations from the Clouds and the Earth’s Radiant Energy System (CERES) program to determine what signals and what natural variability might be expected in the TOA net flux. Based on this analysis, the use of broadband radiation measurements for global climate change studies can be assessed. [ABSTRACT FROM AUTHOR]

This paper reports on an analysis of the tropical cyclone (TC) potential intensity (PI) and its control parameters in transient global warming simulations. Specifically, the TC PI is calculated for phase 3 of the Coupled Model Intercomparison Project (CMIP3) integrations during the first 70 yr of a transient run forced by a 1% yr-1 CO2 increase. The linear trend over the period is used to project a 70-yr change in relevant model parameters. The results for a 15-model ensemble-mean climate projection show that the thermodynamic potential intensity (THPI) increases on average by 1.0% to ∼3.1% over various TC basins, which is mainly attributed to changes in the disequilibrium in enthalpy between the ocean and atmosphere in the transient response to increasing CO2 concentrations. This modest projected increase in THPI is consistent with that found in other recent studies. In this paper the effects of evolving large-scale dynamical factors on the projected TC PI are also quantified, using an empirical formation that takes into account the effects of vertical shear and translational speed based on a statistical analysis of present-day observations. Including the dynamical efficiency in the formulation of PI leads to larger projected changes in PI relative to that obtained using just THPI in some basins and smaller projected changes in others. The inclusion of the dynamical efficiency has the largest relative effect in the main development region (MDR) of the North Atlantic, where it leads to a 50% reduction in the projected PI change. Results are also presented for the basin-averaged changes in PI for the climate projections from each of the 15 individual models. There is considerable variation among the results for individual model projections, and for some models the projected increase in PI in the eastern Pacific and south Indian Ocean regions exceeds 10%. [ABSTRACT FROM AUTHOR]

The wintertime Pacific–North American (PNA) teleconnection pattern has previously been shown to influence springtime snow conditions over portions of North America. This paper develops a more complete physical understanding of this linkage across the continent, using a recently released long-term, continental-scale gridded North American snow depth dataset and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis data. An empirical orthogonal function–based filtering process is used to identify and isolate the interannual snow depth variations associated with PNA. Then linear and partial correlations are employed to investigate the physical mechanisms that link winter PNA with spring snow depth. In the positive phase of PNA, the enhanced PNA pressure centers lead to warmer temperatures over northwestern North America and less precipitation at midlatitudes. The temperature and precipitation pathways act independently and in distinct geographical regions, and together they serve to reduce winter snow depth across much of North America. Winter anomalies in the snow depth field then tend to persist into spring. Dynamic mechanisms responsible for the PNA-influenced North American precipitation and temperature anomalies, involving moisture transport and cold air intrusions, are confirmed in this study and also extended to continental snow depth anomalies. [ABSTRACT FROM AUTHOR]

The dominant interannual El Niño–Southern Oscillation (ENSO) phenomenon and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENSO signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, an ENSO-removal method is developed through which the ENSO signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations are isolated at mid- and low latitudes in the climate parameter fields from observed and reanalysis datasets. This paper addresses the long-term global warming trend (GW); a companion paper concentrates on Pacific pan-decadal variability (PDV). The warming that occurs in the Pacific basin (approximately 0.4 K in the twentieth century) is much weaker than in surrounding regions and the other two ocean basins (approximately 0.8 K). The modest warming in the Pacific basin is likely due to its dynamic nature on the interannual and decadal time scales and/or the leakage of upper ocean water through the Indonesian Throughflow. Based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), a comprehensive atmospheric structure associated with the GW trend is given. Significant discrepancies exist between the two datasets, especially in the tightly coupled dynamics and water vapor fields. The dynamics fields based on NCEP–NCAR, which show a change in the Walker Circulation, are consistent with the GW change in the surface temperature field. However, intensification in the Hadley Circulation is associated with GW trend in ERA-40 instead. [ABSTRACT FROM AUTHOR]

The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882–2001 and climate system model output for 2002–97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors’ strategies. [ABSTRACT FROM AUTHOR]