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In this paper we introduce an objective definition for the onset and the retreat of the northern Australian rainy season that overlaps significantly with the Australian monsoon season. We define onset and retreat dates of the northern Australian rainy season as being the first and the last day of the year when the daily rain rate exceeds and falls below the climatological annual mean rain rate, respectively. However, our definition of onset/demise is not as restrictive as the traditional monsoon season that seeks the arrival of the westerlies and the equatorward retreat of the trough at its onset and demise, respectively. As defined in this paper, the length of the rainy season is longer than the monsoon season and includes the pre- and post-monsoon rainfall. It is noted that an early or later onset date of the northern Australian rainy season is associated with a longer or shorter, wetter or drier, and colder or warmer season, respectively. Similar relationship is also observed with demise date variations, which are, however, weaker than the onset date variations. Furthermore, we find that the relationship of the northern Australian seasonal rainfall variations with ENSO variability becomes stronger when we account for variations in the length of the rainy season compared to the fixed (December-- February) monsoon season length. We also find a significant linear trend over the time period of the analysis from 1901 to 2015 toward an increasing length of the northern Australian rainy season that influences the corresponding rising trend of seasonal rainfall anomalies. [ABSTRACT FROM AUTHOR]

This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records. [ABSTRACT FROM AUTHOR]

The western North Pacific anomalous anticyclone (WNPAC) significantly affects East Asian climate. Previous studies have elucidated interannual variability of the WNPAC associated with El Niño, but decadal variability of the WNPAC remains unknown. The present paper investigates the dominant modes of decadal variability of the WNPAC by using observational data. The first decadal mode, characterized by an anomalous anticyclone centered over the western North Pacific, is associated with the Pacific decadal oscillation (PDO). The relationship between the first mode and the PDO shifted from in phase to out of phase around 1966. From 1900 to 1966 when the PDO and the first mode are in phase, the anticyclone is maintained by the effects of both the strengthened Aleutian low through meridional atmospheric forcing and Indian Ocean warming through enhanced zonal Walker circulation. From 1967 to 2012, the anticyclone is induced by cold SST anomalies over the central equatorial Pacific when the PDO and the first mode are out of phase. The second decadal mode is characterized by an anomalous anticyclone extending from southeastern China to the Philippine Sea and is associated with the Maritime Continent (MC). This anticyclone resides in the sinking branch of the local Hadley circulation, triggered by enhanced convection associated with the MC warming from 1900 to 2012. The finding of the decadal WNPAC in this paper may provide a new way to explain East Asian climate on a decadal time scale. [ABSTRACT FROM AUTHOR]

Evidence from previous climate model simulations has suggested a potentially low efficacy of contrails to force global mean surface temperature changes. In this paper, a climate model with a state-of-the-art contrail cirrus representation is used for fixed sea surface temperature simulations in order to determine the effective radiative forcing (ERF) from contrail cirrus. ERF is expected to be a good metric for intercomparing the quantitative importance of different contributions to surface temperature and climate impact. Substantial upscaling of aviation density is necessary to ensure statistically significant results from our simulations. The contrail cirrus ERF is found to be less than 50% of the respective instantaneous or stratosphere adjusted radiative forcings, with a best estimate of roughly 35%. The reduction of ERF is much more substantial for contrail cirrus than it is for a CO2 increase when both stratosphere adjusted forcings are of similar magnitude. Analysis of all rapid radiative adjustments contributing to the ERF indicates that the reduction is mainly induced by a compensating effect of natural clouds that provide a negative feedback. Compared to the CO2 reference case, a less positive combined water vapor and lapse rate adjustment also contributes to a more distinct reduction of contrail cirrus ERF, but not as much as the natural cloud adjustment. Based on the experience gained in this paper, respective contrail cirrus simulations with interactive ocean will be performed as the next step toward establishing contrail cirrus efficacy. ERF results of contrail cirrus from other climate models equipped with suitable parameterizations are regarded as highly desirable. [ABSTRACT FROM AUTHOR]

This paper shows that joint temperature-precipitation information over a global domain provides a more accurate estimate of aerosol forced responses in climate models than does any other combination of temperature, precipitation, or sea level pressure. This fact is demonstrated using a new quantity called potential detectability, which measures the extent to which a forced response can be detected in a model. In particular, this measure can be evaluated independently of observations and therefore permits efficient exploration of a large number of variable combinations before performing optimal fingerprinting on observations. This paper also shows that the response to anthropogenic aerosol forcing can be separated from that of other forcings using only spatial structure alone, leaving the time variation of the response to be inferred from data, thereby demonstrating that temporal information is not necessary for detection. The spatial structure of the forced response is derived by maximizing the signal-to-noise ratio. For single variables, the north-south hemispheric gradient and equator-to-pole latitudinal gradient are important spatial structures for detecting anthropogenic aerosols in some models but not all. Sea level pressure is not an independent detection variable because it is derived partly from surface temperature. In no case does sea level pressure significantly enhance potential detectability beyond that already possible using surface temperature. Including seasonal or land-sea contrast information does not significantly enhance detectability of anthropogenic aerosol responses relative to annual means over global domains. [ABSTRACT FROM AUTHOR]

The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s. [ABSTRACT FROM AUTHOR]

Future projections of precipitation at regional scales are vital to inform climate change adaptation activities. Therefore, is it important to quantify projected changes and associated uncertainty, and understand model processes responsible. This paper addresses these challenges for southern Africa and the adjacent Indian Ocean focusing on the local wet season. Precipitation projections for the end of the twenty-first century indicate a pronounced dipole pattern in the CMIP5 multimodel mean. The dipole indicates future wetting (drying) to the north (south) of the climatological axis of maximum rainfall, implying a northward shift of the ITCZ and south Indian Ocean convergence zone that is not consistent with a simple "wet get wetter" pattern. This pattern is most pronounced in early austral summer, suggesting a later and shorter wet season over much of southern Africa. Using a decomposition method we determine physical mechanisms underlying this dipole pattern of projected change, and the associated intermodel uncertainty. The projected dipole pattern is largely associated with the dynamical component of change indicative of shifts in the location of convection. Over the Indian Ocean, this apparent northward shift in the ITCZ may reflect the response to changes in the north-south SST gradient over the Indian Ocean, consistent with a "warmest get wetter" mechanism. Over land subtropical drying is relatively robust, particularly in the early wet season. This has contributions from dynamical shifts in the location of convection, which may be related to regional SST structures in the southern Indian Ocean, and the thermodynamic decline in relative humidity. Implications for understanding and potentially constraining uncertainty in projections are discussed. [ABSTRACT FROM AUTHOR]

The westerly jet (WJ) is an important component of atmospheric circulation, which is characterized by prominent seasonal variations in intensity and position. However, the response of the WJ over Asia during the Last Glacial Maximum (LGM) is still not clear. Using general circulation model experiments, the seasonal behaviors of the WJ over central Asia and Japan are analyzed in this paper. The results show that, compared to the present day (PD), the WJ presents a complicated response during the LGM, both in intensity and position. Over central Asia, it becomes weaker in both summer and winter. But over Japan, it is enhanced in summer but becomes diminished in winter. In terms of position, the WJ over central Asia shifts southward in both summer and winter, whereas the WJ over Japan moves southward in summer but does not change obviously relative to PD in winter. Such WJ changes are well explained by meridional temperature gradients in high troposphere, which is closely linked to seasonal thermal anomalies over the Tibetan Plateau (TP). Despite cooler LGM conditions, the anomalous warming center over the TP becomes stronger in summer. Derived from the heat budget equation, the stronger heating center is mainly caused by the weaker adiabatic cooling generated from ascending motion over the area south of the TP. In winter, the cooling over the TP is also strengthened, mostly owing to the subsidence-induced weaker adiabatic heating. Due to the importance of the WJ, the potential role of TP thermal effects should be a focus when explaining the East Asian climate change during the LGM. [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]

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 a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S-80°N, 1979-2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S-80°N, 1979-2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics. [ABSTRACT FROM AUTHOR]

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services. [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]

Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño--like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing. [ABSTRACT FROM AUTHOR]

Temperature observations of the upper-air atmosphere are now available for more than 40 years from both ground- and satellite-based observing systems. Recent years have seen substantial improvements in reducing long-standing discrepancies among datasets throughmajor reprocessing efforts. The advent of radio occultation (RO) observations in 2001 has led to further improvements in vertically resolved temperature measurements, enabling a detailed analysis of upper-troposphere/lower-stratosphere trends. This paper presents the current state of atmospheric temperature trends from the latest available observational records. We analyze observations from merged operational satellite measurements, radiosondes, lidars, and RO, spanning a vertical range fromthe lower troposphere to the upper stratosphere. The focus is on assessing climate trends and on identifying the degree of consistency among the observational systems. The results showa robust cooling of the stratosphere of about 1-3 K, and a robust warming of the troposphere of about 0.6-0.8K over the last four decades (1979- 2018). Consistent results are found between the satellite-based layer-average temperatures and vertically resolved radiosonde records. The overall latitude-altitude trend patterns are consistent between RO and radiosonde records. Significant warming of the troposphere is evident in the RO measurements available after 2001, with trends of 0.25-0.35K per decade. Amplified warming in the tropical upper-troposphere compared to surface trends for 2002-18 is found based on ROand radiosonde records, in approximate agreement withmoist adiabatic lapse rate theory. The consistency of trend results from the latest upper-air datasets will help to improve understanding of climate changes and their drivers. [ABSTRACT FROM AUTHOR]

The Earth's ice-ocean-atmosphere system exhibits nonlinear responses, such as the difference in the magnitude of the atmospheric response to positive or negative ocean or sea ice anomalies. Two classes of methods that have previously been used to investigate the nonlinear dependence between climate fields are kernel methods and neural network methods. In this paper, a third methodology is introduced: gradient-based kernel dimension reduction. Gradient-based kernel methods are an extension of conventional kernel methods, but gradient-based methods focus on the directional derivatives of the regression surface between two fields. Specifically, a new gradient-based method is developed here: gradient kernel canonical correlation analysis (gKCCA). In gKCCA, the canonical directions maximize the directional derivatives between the predictor field and the response field, while the canonical components of the response field maximize the correlation with a nonlinear augmentation of the predictor canonical components. Gradient-based kernel methods have several advantages: their components can be directly related to the original fields (unlike in conventional kernel methods), and the projection vectors are determined by analytical solution (unlike in neural networks). Here gKCCA is applied to the question of nonlinear coupling between high-frequency (2-3 years) and low-frequency (4-6 years) modes in the Pacific Ocean. The leading gKCCA subspace shows a significant nonlinear coupling between the low-pass and high-pass fields. The paper also shows that the results of gKCCA are robust to different levels of noise and different kernel functions. [ABSTRACT FROM AUTHOR]

Hydrological sensitivity is the change in global-mean precipitation per degree of global-mean temperature change. This paper shows that the hydrological sensitivity of the response to anthropogenic aerosol forcing is distinct from that of the combined response to all other forcings and that this difference is sufficient to infer the associated cooling in global-mean temperature. This result is demonstrated using temperature and precipitation data generated by climate models and is robust across different climate models. Remarkably, greenhouse gas warming and aerosol cooling can be estimated in a model without using any spatial or temporal gradient information in the response, provided temperature data are augmented by precipitation data. Over the late twentieth century, the hydrological sensitivities of climate models differ significantly from that of observations. Whether this discrepancy can be attributed to observational error, which is substantial as different estimates of global-mean precipitation are not even significantly correlated with each other, or to model error is unclear. The results highlight the urgency to construct accurate estimates of global precipitation from past observations and for reducing model uncertainty in hydrological sensitivity. This paper also clarifies that previous estimates of hydrological sensitivity are limited in that standard regression methods neglect temperature-precipitation relations that occur through internal variability. An alternative method for estimating hydrological sensitivity that overcomes this limitation is presented. [ABSTRACT FROM AUTHOR]

The assumption of a stationary global signal linked to El Niño–Southern Oscillation (ENSO) events is often used in paleo-ENSO proxy data interpretation. This paper attempts to investigate whether the assumption is valid during the last glacial maximum (LGM) over the region 60°S–90°N, 60°E−60°W. Using four models within phase 3 of the Paleoclimate Modeling Intercomparison Project framework that well reproduce ENSO-induced variabilities, differences from the preindustrial period to LGM in the ENSO-related sea surface temperature pattern and its impacts are investigated. Compared to the preindustrial period, the ENSO impacts are revealed to weaken and shift eastward during the LGM. According to multimodel medians, ENSO impacts on precipitation and near-surface air temperature are attenuated over most regions of concern, with percentage changes in both parameters averaging −21% for the whole region; the ENSO-induced Pacific–North America (PNA) teleconnection pattern is weakened, manifested by the 41% diminished center over the North Pacific and the almost vanished activity centers over the continent. Spatially, there is a zonal contraction of 13° for the sea surface warming of ENSO, as well as eastward migration over 10° for the ENSO-induced positive precipitation anomaly center over the tropical Pacific and the PNA teleconnection pattern outside the tropics. The aforementioned changes are linked to the altered climatic background during the LGM, which features a 16° eastward shift for the Pacific Walker circulation rising branch and a weakened waveguide in the midlatitudes. The results suggest that the hypothesis of stationary ENSO impacts should be applied cautiously to the past. [ABSTRACT FROM AUTHOR]

This paper examines the possible influence of external forcings on observed changes in precipitation extremes in the mid-to-high latitudes of Asia during 1958–2012 and attempts to identify particular extreme precipitation indices on which there are better chances to detect the influence of external forcings. We compare a recently compiled dataset of observed extreme indices with those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations using an optimal fingerprinting method. We consider six indices that characterize different aspects of extreme precipitation, including annual maximum amount of precipitation falling in 1 day (Rx1day) or 5 days (Rx5day), the total amount of precipitation from the top 5% or top 1% daily amount on wet days, and the fraction of the annual total precipitation from these events. For single-signal analysis, the fingerprints of external forcings including anthropogenic agents are robustly detected in most studied extreme indices over all Asia and for midlatitude Asia but not for high-latitude Asia. For two-signal analysis, anthropogenic influence is detectable in these indices over Asia at 5% or slightly less than 5% significance level, whereas natural influence is not detectable. In high-latitude Asia, anthropogenic influence is detected only in a fractional index, representing a stark contrast to the midlatitude and full Asia results. We find relatively smaller internal variability and thus higher signal-to-noise ratio in the fractional indices when compared with the other ones. Our results point to the need for studying precipitation extreme indices that are less affected by internal variability while still representing the relevant nature of precipitation extremes to improve the possibility of detecting a forced signal if one is present in the data. [ABSTRACT FROM AUTHOR]

This study investigates potential biases between equilibrium climate sensitivity inferred from warming over the historical period (ECShist) and the climate system's true ECS (ECStrue). This paper focuses on two factors that could contribute to differences between these quantities. First is the impact of internal variability over the historical period: our historical climate record is just one of an infinity of possible trajectories, and these different trajectories can generate ECShist values 0.3 K below to 0.5 K above (5%–95% confidence interval) the average ECShist. Because this spread is due to unforced variability, I refer to this as the unforced pattern effect. This unforced pattern effect in the model analyzed here is traced to unforced variability in loss of sea ice, which affects the albedo feedback, and to unforced variability in warming of the troposphere, which affects the shortwave cloud feedback. There is also a forced pattern effect that causes ECShist to depart from ECStrue due to differences between today's transient pattern of warming and the pattern of warming at 2×CO2 equilibrium. Changes in the pattern of warming lead to a strengthening low-cloud feedback as equilibrium is approached in regions where surface warming is delayed: the Southern Ocean, eastern Pacific, and North Atlantic near Greenland. This forced pattern effect causes ECShist to be on average 0.2 K lower than ECStrue (~8%). The net effect of these two pattern effects together can produce an estimate of ECShist as much as 0.5 K below ECStrue. [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]

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]

Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM-GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation. [ABSTRACT FROM AUTHOR]

Ninety-five percent of climate models contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) project early summer [October–December (OND)] rainfall declines over subtropical southern Africa by the end of the century, under all emissions forcing pathways. The intermodel consensus underlies the Intergovernmental Panel on Climate Change (IPCC) assessment that rainfall declines are "likely" and implies that significant climate change adaptation is needed. However, model consensus is not necessarily a good indicator of confidence, especially given that there is an order of magnitude difference in the scale of rainfall decline among models in OND (from <10 mm season−1 to ~100 mm season−1), and that the CMIP5 ensemble systematically overestimates present-day OND precipitation over subtropical southern Africa (in some models by a factor of 2). In this paper we investigate the uncertainty in the OND drying signal by evaluating the climate mechanisms that underlie the diversity in model rainfall projections. Models projecting the highest-magnitude drying simulate the largest increases in tropospheric stability over subtropical southern Africa associated with anomalous upper-level subsidence, reduced evaporation, and amplified surface temperature change. Intermodel differences in rainfall projections are in turn related to the large-scale adjustment of the tropical atmosphere to emissions forcing: models with the strongest relative warming of the northern tropical sea surface temperatures compared to the tropical mean warming simulate the largest rainfall declines. The models with extreme rainfall declines also tend to simulate large present-day biases in rainfall and in atmospheric stability, leading the authors to suggest that projections of high-magnitude drying require further critical attention. [ABSTRACT FROM AUTHOR]

This paper scrutinizes the zonal oscillation of the western North Pacific subtropical high (WNPSH) via diagnosing its two extreme phases, which are defined by the top 10% strongest (positive phase) and the weakest (negative phase) WNPSH index (WNPSHI) days during summers in 1979–2016. Key findings include the following: a tripole pattern consisting of intensified (weakened) precipitation over the Maritime Continent and the East Asian summer monsoon regions, and suppressed (strengthened) precipitation over the western North Pacific summer monsoon region during positive (negative) WNPSH phases; a westward movement of WNPSH-induced precipitation anomalies that subsequently affects eastern China, Japan, and the Korean Peninsula at different time lags; an OLR–vorticity pattern explained by atmospheric responses to thermal sources is suggested to drive the oscillation; and the competitive interaction of local air–sea feedbacks, especially during the positive phase. In addition, moderate-to-strong positive correlations between the WNPSHI and the Niño-3.4 index are found on 1–2-, 2–3-, and 3–6-yr time scales; both exhibit decadal shifts to a higher-frequency mode, suggesting the intensification of both the zonal WNPSH oscillation and the ENSO under the changing climate and their close interdecadal association. A nonlinear quasi-biennial WNPSH–ENSO relationship is identified: the positive (negative) WNPSH phase sometimes occurs during 1) a decaying El Niño (La Niña) in the preceding summer/autumn, and/or 2) a developing La Niña (El Niño) in the current summer/autumn. A full ENSO transition from moderate-to-strong El Niño to La Niña is often seen during the positive phase, offering potential in predicting ENSO events and extreme WNPSH phases and thereby the summer monsoon rainfall in East Asia. [ABSTRACT FROM AUTHOR]

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses. [ABSTRACT FROM AUTHOR]

A recent paper suggested that the global climate models used to project future climate changes may significantly overestimate the stability of the Atlantic meridional overturning circulation under anthropogenic global warming. It further asserted that "flux adjusting" the models (adding offsets to heat and freshwater fluxes to reproduce the observed density field) may reduce model stability. However, temperature, salinity, and density fields in climate models likely deviate from observations because of biases in model physics as well as inaccuracies in fluxes. In such cases it is unclear whether adjusting the fluxes to produce a more realistic density field will result in a model with more realistic stability properties, as flux correction may be compensating for other inaccuracies in model formulation. We investigate this question using a simplified dynamical box model, in which we can flux correct one version of the model to match density gradients within another version of the model. We show that flux adjustment can realistically compensate for biases in stability associated with some processes, such as uncertainty in the value of the vertical diffusivity, but not other processes, such as inaccurate simulation of the relationship between density structure and overturning or the isopycnal tracer diffusivity coefficient. An ability to compensate for biases when the overturning shuts off does not imply an ability to compensate for biases in when it reestablishes itself, and vice versa. [ABSTRACT FROM AUTHOR]

The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953-2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas-induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto modelsimulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953-2012 period roughly 23% of the greenhouse gas-induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations. [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]

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

An accurate quantification of the stratospheric ozone feedback in climate change simulations requires knowledge of the ozone response to increased greenhouse gases. Here, an analysis is presented of the ozone layer response to an abrupt quadrupling of CO2 concentrations in four chemistry--climate models. The authors show that increased CO2 levels lead to a decrease in ozone concentrations in the tropical lower stratosphere, and an increase over the high latitudes and throughout the upper stratosphere. This pattern is robust across all models examined here, although important intermodel differences in the magnitude of the response are found. As a result of the cancellation between the upper- and lower-stratospheric ozone, the total column ozone response in the tropics is small, and appears to be model dependent.Asubstantial portion of the spread in the tropical column ozone is tied to intermodel spread in upwelling. The high-latitude ozone response is strongly seasonally dependent, and shows increases peaking in late winter and spring of each hemisphere, with prominent longitudinal asymmetries. The range of ozone responses to CO2 reported in this paper has the potential to induce significant radiative and dynamical effects on the simulated climate. Hence, these results highlight the need of using an ozone dataset consistent with CO2 forcing in models involved in climate sensitivity studies. [ABSTRACT FROM AUTHOR]

It has been known for over a century that the melting of individual ice sheets and glaciers drives distinct geographic patterns, or fingerprints, of sea level change, and recent studies have highlighted the implications of this variability for hazard assessment and inferences of meltwater sources. These studies have computed fingerprints using simplified melt geometries; however, a more generalized treatment would be advantageous when assessing or projecting sea level hazards in the face of quickly evolving patterns of ice mass flux. In this paper the usual fingerprint approach is inverted to compute site-specific sensitivity kernels for a global database of coastal localities. These kernels provide a mapping between geographically variable mass flux across each ice sheet and glacier and the associated static sea level change at a given site. Kernels are highlighted for a subset of sites associated with melting from Greenland, Antarctica, and the Alaska-Yukon-British Columbia glacier system. The latter, for example, reveals an underappreciated sensitivity of ongoing and future sea level change along the U.S. West Coast to the geometry of ice mass flux in the region. Finally, the practical utility of these kernels is illustrated by computing sea level predictions at a suite of sites associated with annual variability in Greenland ice mass since 2003 constrained by satellite gravity measurements. [ABSTRACT FROM AUTHOR]

The sparse nature of observational records across the mid- to high latitudes of the Southern Hemisphere limits the ability to place late-twentieth-century environmental changes in the context of long-term (multidecadal and centennial) variability. Historical records from subantarctic islands offer considerable potential for developing highly resolved records of change. In 1905, a whaling and meteorological station was established at Grytviken on subantarctic South Georgia in the South Atlantic (54°S, 36°W), providing near-continuous daily observations through to present day. This paper reports a new, daily observational record of temperature and precipitation from Grytviken, which is compared to regional datasets and historical reanalysis. The authors find a shift toward increasingly warmer daytime extremes commencing from the mid-twentieth century and accompanied by warmer nighttime temperatures, with an average rate of temperature rise of 0.13°C decade-1 over the period 1907-2016 (p < 0.0001). Analysis of these data and reanalysis products suggest a change of pervasive synoptic conditions across the mid- to high latitudes since the mid-twentieth century, characterized by stronger westerly airflow and associated warm föhn winds across South Georgia. This rapid rate of warming and associated declining habitat suitability has important negative implications for biodiversity, including the survival of key marine biota in the region. [ABSTRACT FROM AUTHOR]

When comparing climate models to observations, it is often observed that the mean over many models has smaller errors than most or all of the individual models. This paper will show that a general consequence of the nonintuitive geometric properties of high-dimensional spaces is that the ensemble mean often outperforms the individual ensemble members. This also explains why the ensemble mean often has an error that is 30% smaller than the median error of the individual ensemble members. The only assumption that needs to be made is that the observations and the models are independently drawn from the same distribution. An important and relevant property of high-dimensional spaces is that independent random vectors are almost always orthogonal. Furthermore, while the lengths of random vectors are large and almost equal, the ensemble mean is special, as it is located near the otherwise vacant center. The theory is first explained by an analysis of Gaussian- and uniformly distributed vectors in high-dimensional spaces. A subset of 17 models from the CMIP5 multimodel ensemble is then used to demonstrate the validity and robustness of the theory in realistic settings. [ABSTRACT FROM AUTHOR]

The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation. [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 NCEP-NCARcomposite dataset (comprising sea level pressure, 500-hPa geopotential height, 500-hPa temperature, and meridional wind stress at 10m above the surface) is used for compiling a set of climate indices describing the most important physical modes of variability in the Southern Hemisphere (SH): the southern annular mode (SAM), semiannual oscillation (SAO), Pacific-South American (PSA), and quasistationary zonal wavenumber 3 (ZW3) patterns. Compelling evidence indicates that the large increase in the SH sea ice, recorded over recent years, arises from the impact of climate modes and their long-term trends. The examination of variability ranging from seasonal to interdecadal scales, and of trends within the climate patterns and total Antarctic sea ice concentration (SIC) for the 32-yr period (1982-2013), is the key focus of this paper. The results herein indicate that a progressive cooling has affected the year-to-year climate of the sub-Antarctic since the 1990s. This feature is found in association with increased positive SAM and SAO phases detected in terms of upward annual and seasonal trends (in autumn and summer) and upward decadal trends. In addition, the SIC shows upward annual, spring, and summer trends, indicating the insulation of Antarctica from the warmer flows in the midlatitudes. This picture of variations is also found to be consistent with the upward trends detected for the PSA and ZW3 patterns on the annual scale and during the last two decades. Evidence of a more frequent occurrence of the PSA-ZW3 combination could explain, in part, the significant increase of the regional and total Antarctic sea ice coverages. [ABSTRACT FROM AUTHOR]

This paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multimodel ensemble. The method regresses each model's estimate of internal variability from the preindustrial control simulation on the variability derived from a model's ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multimodel consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics, and polar regions, and extremely likely decreases in midlatitudes. Applying the method to sea ice volume and area shows that their respective internal variability likely or extremely likely decreases proportionally to their mean state, except for Arctic sea ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice, the method confirms that internal variability can explain most of the models' deviation from observed trends but often not the models' deviation from the observed mean states. The new method benefits from a large number of models and long preindustrial control simulations, but it requires only a small number of ensemble simulations. The method allows for consistent consideration of internal variability in multimodel studies and thus fosters understanding of the role of internal variability in a changing climate. [ABSTRACT FROM AUTHOR]

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al. [ABSTRACT FROM AUTHOR]

Twentieth-century regional sea level changes are estimated from 12 climate models from phase 5 of the Climate Model Intercomparison Project (CMIP5). The output of the CMIP5 climate model simulations was used to calculate the global and regional sea level changes associated with dynamic sea level, atmospheric loading, glacier mass changes, and ice sheet surface mass balance contributions. The contribution from groundwater depletion, reservoir storage, and dynamic ice sheet mass changes are estimated from observations as they are not simulated by climate models. All contributions are summed, including the glacial isostatic adjustment (GIA) contribution, and compared to observational estimates from 27 tide gauge records over the twentieth century (1900-2015). A general agreement is found between the simulated sea level and tide gauge records in terms of interannual to multidecadal variability over 1900-2015. But climate models tend to systematically underestimate the observed sea level trends, particularly in the first half of the twentieth century. The corrections based on attributable biases between observations and models that have been identified in Part I of this two-part paper result in an improved explanation of the spatial variability in observed sea level trends by climate models. Climate models show that the spatial variability in sea level trends observed by tide gauge records is dominated by the GIA contribution and the steric contribution over 1900-2015. Climate models also show that it is important to include all contributions to sea level changes as they cause significant local deviations; note, for example, the groundwater depletion around India, which is responsible for the low twentieth-century sea level rise in the region. [ABSTRACT FROM AUTHOR]

This paper improves an extreme-value-theory-based detection and attribution method and then applies it to four types of extreme temperatures, annual minimum daily minimum (TNn) and maximum (TXn) and annual maximum daily minimum (TNx) and maximum (TXx), using the HadEX2 observation and the CMIP5 multimodel simulation datasets of the period 1951-2010 at 17 subcontinent regions. The methodology is an analog of the fingerprinting method adapted to extremes using the generalized extreme value (GEV) distribution. The signals are estimated as the time-dependent location parameters of GEV distributions fitted to extremes simulated by multimodel ensembles under anthropogenic (ANT), natural (NAT), or combined anthropogenic and natural (ALL) external forcings. The observed extremes are modeled by GEV distributions whose location parameters incorporate the signals as covariates. A coordinate descent algorithm improves both computational efficiency and accuracy in comparison to the existing method, facilitating detection of multiple signals simultaneously. An overall goodness-of-fit test was performed at the regional level. The ANT signal was separated from the NAT signal in four to six regions. In these analyses, the waiting times of the 1951-55 20-yr return level in the 2006-10 climate for the temperature of the coldest night and day were found to have increased to over 20 yr; the corresponding waiting times for the warmest night and day were found to have dropped below 20 yr in a majority of the regions. [ABSTRACT FROM AUTHOR]

The role of stationary planetary waves in the dynamical response of the Arctic winter stratosphere circulation to global warming is investigated here by analyzing simulations performed with atmosphere-only models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) driven by prescribed sea surface temperatures (SSTs). Climate models often simulate dynamical warming of the Arctic stratosphere as a response to global warming in association with a strengthening of the deep branch of the Brewer-Dobson circulation; however, until now, no satisfactory mechanism for such a response has been suggested. This study focuses on December-February (DJF) because this is the period when the troposphere and stratosphere are strongly coupled. When forced by increased SSTs, all the models analyzed here simulate Arctic stratosphere dynamical warming, mostly due to increased upward propagation of quasi-stationary wavenumber 1, as diagnosed by the meridional eddy heat flux. Further, it is shown that the stratospheric warming and increased wave flux to the stratosphere are related to the strengthening of the zonal winds in subtropics and midlatitudes near the tropopause. Evidence presented in this paper corroborate climate model simulations of future stratospheric changes and suggest a dynamical warming of the Arctic polar vortex as the most likely response to global warming. [ABSTRACT FROM AUTHOR]

Kretzschmar et al., in a comment in 2017, use the spread in the output of aerosol-climate models to argue that the models refute the hypothesis (presented in a paper by Stevens in 2015) that for the mid-twentieth-century warming to be consistent with observations, then the present-day aerosol forcing, must be less negative than −1 W m−2. The main point of contention is the nature of the relationship between global SO2 emissions and In contrast to the concave (log-linear) relationship used by Stevens and in earlier studies, whereby becomes progressively less sensitive to SO2 emissions, some models suggest a convex relationship, which would imply a less negative lower bound. The model that best exemplifies this difference, and that is most clearly in conflict with the hypothesis of Stevens, does so because of an implausible aerosol response to the initial rise in anthropogenic aerosol precursor emissions in East and South Asia-already in 1975 this model's clear-sky reflectance from anthropogenic aerosol over the North Pacific exceeds present-day estimates of the clear-sky reflectance by the total aerosol. The authors perform experiments using a new (observationally constrained) climatology of anthropogenic aerosols to further show that the effects of changing patterns of aerosol and aerosol precursor emissions during the late twentieth century have, for the same global emissions, relatively little effect on These findings suggest that the behavior Kretzschmar et al. identify as being in conflict with the lower bound in Stevens arises from an implausible relationship between SO2 emissions and and thus provides little basis for revising this lower bound. [ABSTRACT FROM AUTHOR]

Global climate model simulations project that the tropical Andes Mountains of South America, which are particularly vulnerable to climate change because of a reliance on snow and glacial melt for freshwater resources, will experience enhanced warming in the near future, with both higher rates of warming at higher elevations within the mountain range itself and localized enhancement of warming exceeding surrounding areas of the globe. Yet recent surface temperature changes in the tropical Andes do not show evidence for either elevation-dependent warming or regional enhancement of warming on average. However, it remains a possibility that the expected warming trends in this region have begun to manifest in other ways (e.g., in the free atmosphere or at intermediate mountain elevations). This paper proposes evidence from several reanalysis products that there has indeed been a regional enhancement of midtropospheric warming around the central Andes over the past few decades that makes this region stand out as a hot spot within the broader pantropics. This trend is generally not reproduced by historical AMIP climate model simulations, which suggests that the mechanisms through which the atmosphere is warming over the central Andes are not adequately captured by climate models. Possible explanations for localized enhancement of warming in this region are considered. On the other hand, reanalysis products do not consistently exhibit enhanced warming at intermediate mountain elevations in the central Andes as evidenced by the generally moderate rates of change in the freezing-level height, except perhaps in the highest-resolution reanalysis product. [ABSTRACT FROM AUTHOR]

This paper investigates changes in the tropical tropopause layer (TTL) in response to carbon dioxide increase and surface warming separately in an atmospheric general circulation model, finding that both effects lead to a warmer tropical tropopause. Surface warming also results in an upward shift of the tropopause. A detailed heat budget analysis is performed to quantify the contributions from different radiative and dynamic processes to changes in the TTL temperature. When carbon dioxide increases with fixed surface temperature, a warmer TTL mainly results from the direct radiative effect of carbon dioxide increase. With surface warming, the largest contribution to the TTL warming comes from the radiative effect of the warmer troposphere, which is partly canceled by the radiative effect of the moistening at the TTL. Strengthening of the stratospheric circulation following surface warming cools the lower stratosphere dynamically and radiatively via changes in ozone. These two effects are of comparable magnitudes. This circulation change is the main cause of temperature changes near 63 hPa but is weak near 100 hPa. Contributions from changes in convection and clouds are also quantified. These results illustrate the heat budget analysis as a useful tool to disentangle the radiative-dynamical-chemical-convective coupling at the TTL and to facilitate an understanding of intermodel difference. [ABSTRACT FROM AUTHOR]

This paper develops a method for homogenizing daily temperature series. While daily temperatures are statistically more complex than annual or monthly temperatures, techniques and computational methods have been accumulating that can now model and analyze all salient statistical characteristics of daily temperature series. The goal here is to combine these techniques in an efficient manner for multiple changepoint identification in daily series; computational speed is critical as a century of daily data has over 36 500 data points. The method developed here takes into account 1) metadata, 2) reference series, 3) seasonal cycles, and 4) autocorrelation. Autocorrelation is especially important: ignoring it can degrade changepoint techniques, and sample autocorrelations of day-to-day temperature anomalies are often as large as 0.7. While daily homogenization is not conducted as commonly as monthly or annual homogenization, daily analyses provide greater detection precision as they are roughly 30 times as long as monthly records. For example, it is relatively easy to detect two changepoints less than two years apart with daily data, but virtually impossible to flag these in corresponding annually averaged data. The developed methods are shown to work in simulation studies and applied in the analysis of 46 years of daily temperatures from South Haven, Michigan. [ABSTRACT FROM AUTHOR]

In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often outperforms at least half of the ensemble members in mean squared error (measured with respect to observations). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often outperforms all or almost all of the ensemble members across a range of climate variables. This paper shows that these could be mathematical results based on convexity and averaging but with implications for the properties of the current generation of climate simulators. [ABSTRACT FROM AUTHOR]

Reconstructions of historical climate changes indicate that surface air temperatures decreased over the preindustrial last millennium. Conflicting explanations have been proposed for the cause of the transition from the Medieval Climate Anomaly (MCA) in the early part of the last millennium to the Little Ice Age (LIA) near its end. The possible causes include volcanic emissions, total solar irradiance (TSI) variations, greenhouse gas concentration fluctuations, and orbital forcing variations. In the present paper, it is demonstrated that all of these climate forcings contribute significantly to simulated surface air temperature (SAT) and sea ice concentration changes over this period. On the other hand, simulated ocean heat content appears to respond significantly only to volcanic and TSI variations. In model simulations at T85 spectral resolution, TSI reductions and volcanic emissions together generate significant increases in sea ice extent in the Barents Sea, which is found to be responsible for most of the temperature reductions over northwestern Europe. TSI appears less important to Arctic sea ice and SAT changes in simulations at T42 spectral resolution, which are weaker than at T85 resolution. Such resolution dependence is attributed to differences in background conditions in the responses to these external climate forcings. Nonlinearities in the forcing responses and sensitivities to background conditions challenge the assumption that sensitivity tests for given forcings can be run independently. Additionally, it is demonstrated that an ensemble of model simulations is required to isolate forcing responses even over a period as long as the last millennium. [ABSTRACT FROM AUTHOR]

Large-scale synoptic circulations have a profound effect on western U.S. summer weather and climate. Heat waves, water availability, the distribution of monsoonal moisture, fire-weather conditions, and other phenomena are impacted by the position and amplitude of large-scale synoptic circulations. Furthermore, regional weather is modulated by the interactions of the large-scale flow with terrain and land-water contrasts. It is therefore crucial to understand projected changes in large-scale circulations and their variability under anthropogenic global warming. Although recent research has examined changes in the jet stream, storm tracks, and synoptic disturbances over the Northern Hemisphere under global warming, most papers have focused on the cold season. In contrast, this work analyzes the projected trends in the spatial distribution and amplitude of large-scale synoptic disturbances over the western United States and eastern Pacific during July and August. It is shown that CMIP5 models project weaker mean midtropospheric gradients in geopotential height as well as attenuated temporal variability in geopotential height, temperature, vorticity, vertical motion, and sea level pressure over this region. Most models suggest reduced frequency of troughs and increased frequency of ridges over the western United States. These changes in the variability of synoptic disturbances have substantial implications for future regional weather and climate. [ABSTRACT FROM AUTHOR]

Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December-February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross-time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden-Julian oscillation, and combinations of both). Spatially averaged Kendall's τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur. [ABSTRACT FROM AUTHOR]

Changes in internal variability of seasonal and annual mean 2-m temperature in response to anthropogenic forcing are quantified for a global domain using climate models driven by a twenty-first-century high-emissions scenario. While changes in variance have been quantified previously in a univariate sense, the field significance of such changes has remained unclear. This paper proposes a new field significance test for changes in variance that accounts for spatial and temporal relationships within the domain. The test proposed here uses an optimization technique based on discriminant analysis, yielding results that are invariant to linear transformations of the data and therefore independent of normalization procedures. Multiple significance tests are employed because spatial fields can differ in many ways in a multivariate space. All climate models investigated here predict significant changes in internal variability of temperature in response to anthropogenic forcing. The models consistently predict decreases to temperature variance in regions of seasonal sea ice formation and across the Southern Ocean by the end of the twenty-first century. While more than half the models also predict significant changes in variance over ENSO regions and the North Atlantic Ocean, the direction of this change is model dependent. Seasonal mean changes are remarkably similar to annual mean changes, but there are model-dependent exceptions. Some models predict future variability that is more than double their preindustrial control variability, raising questions about the adequacy of doubling uncertainty estimates to test robustness in detection and attribution studies. [ABSTRACT FROM AUTHOR]