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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]

Does a warming world, where extremely hot summers are becoming more common, mean that cold summers will never again occur? It is crucial to know whether extremely cold summers are still possible, as such knowledge will significantly impact decisions regarding the further adaptation of crops to cold summers. Japan, which has suffered from many extremely cold summers, has managed past agricultural disruptions with emergency rice imports. In this paper, we show that a climate regime shift associated with the positive phase shift of the summer Arctic Oscillation occurred in 2010 in northeast Eurasia, making the occurrence of extremely cold summers highly unlikely as long as this new regime persists. In fact, Japan has not experienced a cold summer since 2010, while extremely hot summers have been frequent. Since 2010, a double-jet structure with subtropical and polar jets has strengthened, and the polar jet has meandered farther north of Japan, resulting in an upper-tropospheric anticyclone. This anticyclone, which extends downward and tilts southward, reaches southern Japan and prevents cold advection of oceanic air over the cold Oyashio. The Okhotsk high, known as the leading cause of cold summers, has occurred frequently in recent years; however, cold summers have not occurred due to the tilting anticyclone. The recent warming of the Oyashio weakens cold advection. The Pacific–Japan pattern, known as a remote tropical influence, has been weakened. A better understanding of the regime shift will help us understand the tilting anticyclone and the associated extreme summers in northeast Eurasia. Significance Statement: Extremely cold summers are among the most destructive natural disasters, both socioeconomically and agriculturally. Historically, food shortages due to cold summers have triggered wars. This paper proposes that a hemispheric-scale climate regime shift occurred in or around 2010. This regime shift has included warmings in the North Pacific and East Eurasian land surface temperatures. The regime shift is accompanied by the positive shift of the Arctic Oscillation (AO), a jet meander, and an upper-tropospheric anticyclone, making eastern Eurasia extremely hot. Our results imply that extremely cold summers are unlikely to occur in eastern Eurasia so long as this regime persists. Moving forward, it is important that the link between this regime shift and global warming be explored. [ABSTRACT FROM AUTHOR]

The climate system responds to changes in the amount of atmospheric greenhouse gases or aerosols through rapid processes, triggered within hours and days, and through slower processes, where the full response may only be seen after centuries. In this paper, we aim to elucidate the mechanisms operating on time scales of hours to years to better understand the response of key climate quantities such as energy fluxes, temperature, and precipitation after a sudden increase in either carbon dioxide (CO2), black carbon (BC), or sulfate (SO4) aerosols. The results are based on idealized simulations from six global climate models. We find that the effect of changing ocean temperatures kicks in after a couple of months. Rapid precipitation reductions start occurring instantly and are established after just a few days. For BC, they constitute most of the equilibrium response. For CO2 and SO4, the magnitude of the precipitation response gradually increases as surface warming/cooling evolves, and for CO2, the sign of the response changes from negative to positive after 2 years. Rapid cloud adjustments are typically established within the first 24 h, and while the magnitude of cloud feedbacks for CO2 and SO4 increases over time, the geographical pattern of the equilibrium cloud change is present already after the first year. While there are model differences, our work underscores the overall similarity of the major time-varying processes and responses simulated by current global models and hence the robustness of key features of simulated responses to historical and future anthropogenic forcing. Significance Statement: How does the climate system respond to a change in the amount of atmospheric greenhouse gases or aerosols? Some processes are rapid, responding within hours and days. Others are slow, and the full response to a forcing of the climate may only be seen after centuries. In this paper, we use six global climate models to investigate the time scales of climate responses to carbon dioxide, black carbon, and sulfate, focusing on key climate quantities, such as temperature, precipitation, and clouds. While there are ample model differences, our work underscores the overall similarity of the major time-varying processes and responses simulated by current global models and hence the robustness of key features of simulated responses to historical and future anthropogenic forcing. [ABSTRACT FROM AUTHOR]

This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-yr intervals (85th, 90th, and 95th percentiles and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95th-percentile daily maximum temperature shows a warming rate of up to 0.2°C decade−1 above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land–atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of the Weather Research and Forecasting (WRF) Model simulations forced by 12 global climate model simulations from phase 5 of the Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing. Significance Statement: The heatwave of June 2021 had substantial societal and ecological impacts, illustrating vulnerability to record-shattering events. This paper addresses whether climate change can cause heat events in the Pacific Northwest to warm at a more rapid rate than typical days. Based on observations, in the recent past, the hottest days warmed at about the same rate as typical days in the recent past. However, results from a high-resolution climate model show a higher rate of warming during the warmest days relative to median days under the most aggressive future emissions scenario. This effect could cause heatwaves to become more intense relative to typical days by 2100, making adaptation to extreme events more difficult. [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]

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 describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [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]

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]

This paper examines the processes that drive Arctic anomalous surface warming and sea ice loss during winter-season tropospheric energy flux events, synoptic periods of increased tropospheric energy flux convergence (Ftrop), using the NASA MERRA-2 reanalysis. During an event, a poleward anomaly in Ftrop initially increases the sensible and latent energy of the Arctic troposphere; as the warm and moist troposphere loses heat, the anomalous energy source is balanced by a flux upward across the tropopause and a downward net surface flux. A new metric for the Arctic surface heating efficiency (Etrop) is defined, which measures the fraction of the energy source that reaches the surface. Composites of high-, medium-, and low-efficiency events help identify key physical factors, including the vertical structure of Ftrop and Arctic surface preconditioning. In high-efficiency events (Etrop ≥ 0.63), a bottom-heavy poleward Ftrop occurs in the presence of an anomalously warm and unstratified Arctic—a consequence of decreased sea ice—resulting in increased vertical mixing, enhanced near-surface warming and moistening, and further sea ice loss. Smaller Etrop, and thus weaker surface impacts, are found in events with anomalously large initial sea ice extent and more vertically uniform Ftrop. These differences in Etrop are manifested primarily through turbulent heat fluxes rather than downward longwave radiation. The frequency of high-efficiency events has increased from the period 1980–99 to the period 2000–19, contributing to Arctic surface warming and sea ice decline. [ABSTRACT FROM AUTHOR]

Global climate models, like any in silico numerical experiments, are affected by different types of bias. Uncertainty quantification remains a challenge in any climate detection and attribution analysis. A fundamental methodological question is to determine which statistical summaries, while bringing relevant signals, can be robust with respect to multimodel errors. In this paper, we propose a simple statistical framework that significantly improves signal detection in climate attribution studies. We show that the complex bias correction step can be entirely bypassed for models for which bias between the simulated and unobserved counterfactual worlds is the same as between the simulated and unobserved factual worlds. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. The detected anthropogenic signal in yearly maxima of daily precipitation clearly emerges at the beginning of the twenty-first century. In addition, no CMIP model seems to outperform the others and a weighted linear combination of all improves the estimation of emergence times. Significance Statement: We show that the bias in multimodel global climate simulations can be efficiently handled when the appropriate metric is chosen. This metric leads to an easy-to-implement statistical procedure based on a checkable assumption. This allows us to demonstrate that optimal convex combinations of CMIP outputs can improve the signal strength in finding emergence times. Our data analysis procedure is applied to yearly maximum of precipitation from CMIP5 and CMIP6 databases. The attribution of the anthropogenic forcing clearly emerges in extreme precipitation at the beginning of the twenty-first century. [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]

Projections of future West African monsoon (WAM) precipitation change in response to increasing greenhouse gases are uncertain, and an improved understanding of the drivers of WAM precipitation change is needed to help aid model development and better inform adaptation policies in the region. This paper addresses one of those drivers, the direct radiative effect of increased CO2 (i.e., the impact of increased CO2 in the absence of SST warming and changes in plant physiology). An atmosphere-only model is used to examine both the equilibrium response and the evolution of the change over the days following the instantaneous CO2 increase. In response to the direct radiative effect, WAM precipitation increases due to a weakening of the shallow meridional circulation over North Africa, advecting less dry air into the convective column associated with the monsoon. Changes in the shallow circulation are associated with atmospheric and surface warming patterns over North Africa. A large-scale atmospheric warming pattern, whereby North Africa warms more than the monsoon region, leads to a northward shift in the Saharan heat low. In response to increased precipitation in the Sahel, local soil moisture feedbacks play a key role in determining the low-level circulation change and the location of the intertropical discontinuity. The large-scale warming patterns over North Africa result from differing levels of constraint applied by convective quasi-equilibrium. While this constraint acts strongly in the equatorial WAM region, preventing the region from warming in response to the direct radiative effect, North Africa is not strongly constrained and is therefore able to warm. [ABSTRACT FROM AUTHOR]

Global warming projections of dynamics are less robust than projections of thermodynamics. However, robust aspects of the thermodynamics can be used to constrain some dynamical aspects. This paper argues that tropospheric expansion under global warming (a thermodynamical process) explains changes in the amplitude of the quasi-biennial oscillation (QBO) in the lower and middle stratosphere (a dynamical process). A theoretical scaling for tropospheric expansion of approximately 6 hPa K−1 is derived, which agrees well with global climate model (GCM) experiments. Using this theoretical scaling, the response of QBO amplitude to global warming is predicted by shifting the climatological QBO amplitude profile upward by 6 hPa per kelvin of global warming. In global warming simulations, QBO amplitude in the lower to middle stratosphere shifts upward as predicted by tropospheric expansion. Applied to observations, the tropospheric expansion framework suggests a historical weakening of QBO amplitude at 70 hPa of 3% decade−1 from 1953 to 2020. This expected weakening trend is half of the 6% decade−1 from 1953 to 2012 detected and attributed to global warming in a recent study. The previously reported trend was reinforced by record low QBO amplitudes during the mid-2000s, from which the QBO has since recovered. Given the modest weakening expected on physical grounds, past decadal modulations of QBO amplitude are reinterpreted as a hitherto unrecognized source of internal variability. This large internal variability dominates over the global warming signal such that, despite 65 years of observations, there is not yet a statistically significant weakening trend. [ABSTRACT FROM AUTHOR]

This paper introduces a set of descriptors applied to weather regimes that allow for a detailed monitoring of the location and intensity of their atmospheric centers of action (e.g., troughs and ridges) and the gradients between them, when applicable. Descriptors are designed to document the effect of climate variability and change in modulating the character of daily weather regimes, rather than merely their occurrence statistics. As a case study, the methodology is applied to Aotearoa New Zealand (ANZ), using ERA5 ensemble reanalysis data for the period 1979-2019. Here, we analyze teleconnections between the regimes and their descriptors and large-scale climate variability. Results show a significant modulation of centers of action by the phase of the southern annular mode, with a strong relationship identified with the latitude of atmospheric ridges. Significant associations with El Niño-Southern Oscillation are also identified. Modes of large-scale variability have a stronger influence on the regimes' intrinsic features than their occurrence. This demonstrates the usefulness of such descriptors, which help explain the relationship betweenmidlatitude transient perturbations and large-scale modes of climate variability. In future research, this methodological framework will be applied to analyze (i) low-frequency changes in weather regimes under climate change, in line with the southward shift of storm tracks, and (ii) regional-scale effects on the climate of ANZ, resulting from interaction with its topography. [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]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [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]

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]

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]

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]

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]

This paper examines the controls on global precipitation that are evident in the transient experiments conducted using coupled climate models collected for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The change in precipitation, water vapor, clouds, and radiative heating of the atmosphere evident in the 1% increase in carbon dioxide until doubled (1pctto2x) scenario is examined. As noted in other studies, the ensemble-mean changes in water vapor as carbon dioxide is doubled occur at a rate similar to that predicted by the Clausius–Clapeyron relationship. The ratio of global changes in precipitation to global changes in water vapor offers some insight on how readily increased water vapor is converted into precipitation in modeled climate change. This ratio ℇ is introduced in this paper as a gross indicator of the global precipitation efficiency under global warming. The main findings of this paper are threefold. First, increases in the global precipitation track increase atmospheric radiative energy loss and the ratio of precipitation sensitivity to water vapor sensitivity is primarily determined by changes to this atmospheric column energy loss. A reference limit to this ratio is introduced as the rate at which the emission of radiation from the clear-sky atmosphere increases as water vapor increases. It is shown that the derived efficiency based on the simple ratio of precipitation to water vapor sensitivities of models in fact closely matches the sensitivity derived from simple energy balance arguments involving changes to water vapor emission alone. Second, although the rate of increase of clear-sky emission is the dominant factor in the change to the energy balance of the atmosphere, there are two important and offsetting processes that contribute to ℇ in the model simulations studied: One involves a negative feedback through cloud radiative heating that acts to reduce the efficiency; the other is the global reduction in sensible heating that counteracts the effects of the cloud feedback and increases the efficiency. These counteracting feedbacks only apply on the global scale. Third, the negative cloud radiative heating feedback occurs through reductions of cloud amount in the middle troposphere, defined as the layer between 680 and 440 hPa, and by slight global cloud decreases in the lower troposphere. These changes act in a manner to expose the warmer atmosphere below to high clouds, thus resulting in a net warming of the atmospheric column by clouds and a negative feedback on the precipitation. [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]

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]

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]

Atmospheric general circulation models (AGCMs) and coupled general circulation models (CGCMs) in the High Resolution Model Intercomparison Project (HighResMIP) were evaluated on their ability to simulate tropical cyclone (TC) activity in the western North Pacific (WNP) over its annual cycle. Specifically, we examined these models' ability to simulate the south–north migration of the mean TC genesis location. The results revealed that both types of models realistically captured TC numbers and the south–north migration of TC genesis locations associated with the meridional migration of the WNP subtropical high ridge in response to the annual cycle. However, TC numbers decreased less rapidly in the AGCMs than in both the CGCMs and observed data during the monsoon retreat period (after September). This bias was probably attributed to a low-tropospheric cyclonic anomaly over the Philippine Sea in response to La Niña–like sea surface temperature (SST) differences between the AGCMs and the CGCMs. Because of these differences, the TC genesis frequency in the AGCMs over the Philippine Sea was overestimated. The cyclonic anomaly occurred when the northeasterly trade wind arose and was maintained through wind–evaporation–SST feedback. In future climate (2021–50), the main changes occurred during the monsoon retreat period. A more rapid decrease in TC numbers shown in AGCMs was likely attributed to the decrease (increase) of low-level vorticity (vertical wind shear) modulated by enhanced WNP subtropical high and subsidence anomalies. Conversely, a cyclonic circulation and ascending anomalies projected by CGCMs were identified off-equator, which favored TC genesis location shifting northward. Significance Statement: Model performance and future changes in the annual cycle of tropical cyclone (TC) activity in the western North Pacific are investigated. We found that high-resolution uncoupled and coupled models realistically captured the TC numbers and meridional migration of TC genesis locations associated with the meridional migration of subtropical high ridge. However, TC numbers decreased more gradually in the uncoupled models than in the coupled models after September. This lower skill may stem from differences between the uncoupled and coupled models in simulating a cyclonic anomaly over the Philippine Sea in response to the La Niña–like sea surface temperature difference. In future climate (2021–50), TC numbers are significantly projected to decrease more rapidly during the monsoon retreat period in uncoupled models. [ABSTRACT FROM AUTHOR]

To understand how extratropical cyclones (ETCs) may change in a warmer climate, we conduct idealized baroclinic life cycle simulations using the Icosahedral Nonhydrostatic (ICON)–NWP model with varied initial conditions. With respect to a present-day climate, two experiments are highlighted: a 4-K uniform warming and a more realistic late twenty-first-century warming pattern projected by a Coupled Model Intercomparison Project, version 6 (CMIP6), model. Different ETC deepening mechanisms, especially by diabatic processes, are quantified via the pressure tendency equation analysis, and the horizontal model resolution dependency is examined by contrasting coarse-grid (80 km) and convection-permitting (2.5 km) simulations. While our simulated ETCs are primarily baroclinically driven, dominated by the horizontal warm-air advection in the air column above the surface low, such an effect only strengthens by 10% in both warming experiments. However, the direct contribution of diabatic heating to surface pressure drop almost doubles, which likely feeds back positively to horizontal warm-air advection. Although their combined response to warming is pronounced, it is largely offset by the strengthened adiabatic cooling (17%) due to enhanced upward motions in warmer and moister ETCs, leading to a marginal ETC deepening at maturity (lowers by ∼1.5–4 hPa). Nevertheless, the near-surface impacts strongly increase, particularly the local extreme precipitation (up to 56%). The convection-permitting and the coarse-grid simulations show qualitatively consistent ETC responses to global warming. We suggest that the systematically weaker ETCs (with higher central pressure) in 2.5-km compared to 80-km simulations might be primarily caused by model uncertainty in representing the convective-diabatic heating over the warm front near the cyclone core. [ABSTRACT FROM AUTHOR]

Observed precipitation changes in the Southeast United States (SEUS) are spatially heterogeneous. Most of the inland SEUS and eastern Gulf Coast become drier, and the East Coast north of Charleston, South Carolina, and southern Florida become wetter from the old 30-yr period of 1961–90 to the recent period of 1991–2020. The observed climate change is examined from the perspective of daily weather types (WTs). A k-means clustering analysis has been conducted using daily 850-hPa circulation for 1948–2021. The obtained 10 WTs peak in different seasons, respectively. The frequencies and precipitation intensity of the WTs have been analyzed. A winter WT characterized by a western Appalachian trough (WAT) and a summer WT featuring North Atlantic subtropical high (NASH) have a rising trend of annual frequency from 1948 to 2021. An Appalachian high in the autumn has a decreasing frequency but becomes drier and stronger. Some precipitation intensity change and small location shift have also been observed. The drying up on the eastern Gulf Coast and the inland area of the SEUS is mainly caused by the weakened southwesterly low-level jet (LLJ) on the western flank of the NASH that reduces rain in the spring, the less frequent but stronger and drier Appalachian high in the summer and autumn, and the weaker and more western located Plains trough (PT) in the winter, spring, and autumn. The precipitation increase in the East Coast and southern Florida is majorly due to more frequent, stronger, and rainier troughs along the western Appalachian as well as the East Coast. [ABSTRACT FROM AUTHOR]

Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to 1) changes in the total energy/energy flux, 2) the base-climate temperature/humidity partitioning, and 3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower-tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surface fluxes, a key component of the land surface perspective. Significance Statement: Understanding what conditions give rise to the largest magnitude of warming in response to rising CO2 concentrations is not only scientifically important but also critical from a climate impact standpoint. Two main perspectives, one focusing on atmospheric dynamics and the other focusing on land surface processes, have been proposed to explain the stronger warming associated with drier conditions in the tropics. Here, we compare and contrast these two perspectives. We demonstrate that amplified warming in CMIP6 models can largely be predicted from base-climate dryness alone in both perspectives but is further modified based on changes in the partitioning of energy between temperature and moisture. We highlight the spatiotemporal conditions where assumptions in the two perspectives hold and where deviations occur within CMIP6 climate models. [ABSTRACT FROM AUTHOR]

East Asian summer monsoon precipitation is projected to increase under greenhouse warming with strong intraseasonal variation. Using a 35-member CESM Large Ensemble and 30 CMIP6 models, this study reveals that in July and August, maximum rainfall changes in East Asia take place in the midlatitudes, influencing regions encompassing North and Northeast China, the Korean Peninsula, and Japan. The intensified precipitation is attributed to the combined effect of the thermodynamic and dynamic components. The former stems from the enriched low-level moisture, which peaks in continental East Asia in July and August, under global warming. The dynamic effect is due to the enhanced upward motion, associated with the enhanced southerlies throughout the troposphere over midlatitude East Asia. The southerlies also act to intensify the low-level monsoonal circulation, strengthening moisture transport from the tropical ocean to the midlatitudes. In addition to the mean-state changes, the precipitation interannual variability in this region also intensifies, partly due to the enhanced low-level moisture and partly associated with enhanced large-scale circulation anomalies, such as the northwestern Pacific anticyclone. The enhanced background precipitation, along with the intensified interannual variability, may lead to more rainy summers in a warmer climate, with instances where historically extreme precipitation events become more frequent, posing challenges for water resource management and agriculture in the region. [ABSTRACT FROM AUTHOR]

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%–7% (relative to a 2000–20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (∼5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026–50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from −25% to +40%, contrasting with a range from −30% to −5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000–20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades. Significance Statement: Increasing temperatures have led to concerns that Colorado River flows will be permanently reduced due to global warming. Here, we analyze precipitation, surface temperature, and streamflow (at Lees Ferry) in the upper Colorado River basin since 1895 revealing precipitation to be the largest contributor to runoff variability while temperature variability has been a much smaller contributor. New climate model projections indicate increased precipitation in the upper basin due to global warming, acting to increase river flows by 5%–7% during 2026–50. This wet signal compensates for some of the depleting effects from further basinwide warming. Critically, the large intrinsic variability in precipitation is demonstrated to yield a wider range of future Colorado River flows than previously foreseen based solely on considering temperature effects. [ABSTRACT FROM AUTHOR]

Recent research has shown that there is a projected increase in the frequency of strong positive Indian Ocean dipole (spIOD) events in terms of both sea surface temperature (SST) and precipitation. However, it is not clear whether the spIOD events defined by SST and precipitation, hereafter referred to as SST-spIOD and Pr-spIOD events, respectively, will increase at the same pace under greenhouse warming. Using climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and CMIP6 that can reasonably simulate the spIOD events, here we find that the occurrence of Pr-spIOD events increases much more than that of SST-spIOD events (+217% vs +77%) under the future high-emission scenario. The diverse response stems from a notably large increase in the spIOD events with strong rainfall but moderate SST anomalies (Pr-only spIOD), which is facilitated by a change in the local meridional Hadley circulation induced by land–sea thermal contrast in addition to the background mean-state SST warming. For the spIOD events with prominent anomalies in both rainfall and SST (concurrent spIOD), their occurrences are projected to increase, but their average SST amplitude is projected to weaken. The increased occurrence is associated with a westward-extended structural change induced by mean linear zonal advection feedbacks, while the weakened event-average SST amplitude results from a more stabilized atmosphere and coincides with the projected reduction in IOD SST skewness. Our results suggest that IOD-related mitigation strategies should consider the diverse responses of different kinds of spIOD events to greenhouse warming. [ABSTRACT FROM AUTHOR]

Accurately assessing cyclone intensity changes due to global warming is crucial for predicting and mitigating sequential hazards. This study develops a high-resolution, fully coupled air–sea model to investigate the impact of global warming on Supertyphoon Mangkhut (2018). A numerical sensitivity analysis is conducted using the pseudo–global warming (PGW) technique based on multiple global climate models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). Under ocean warming scenarios, the increasing average sea surface temperature (SST) by 2.26°, 2.44°, 3.45°, and 4.53°C results in reductions in the minimum sea level pressure by 9.2, 10.6, 15.7, and 19.4 hPa, respectively, compared to the original state of Typhoon Mangkhut. Rising SST increases the turbulent heat flux; to be specific, an average SST increase of 2.26°–4.53°C changes the turbulent heat flux into 177%–272% of the original value. Besides, stronger winds enhance SST cooling, including upwelling and entrainment, leading to an increase in the mixed layer depth (MLD). Tropical cyclone heat potential (TCHP) tends to be enhanced under the combined influences as the SST rises. An average increase in the SST of 2.26°, 2.44°, 3.45°, and 4.53°C leads to an increase in the TCHP of 9.94%, 9.85%, 14.67%, and 15.30%, respectively. However, future changes in atmospheric temperature and humidity will moderate typhoon intensification induced by ocean warming. Considering atmospheric conditions, the maximum wind speed decreases by approximately 10% compared to only considering ocean warming. Nevertheless, typhoon intensity is projected to strengthen under future climate change. Significance Statement: This study examines the role of global warming in typhoon intensity and the response of typhoon events to changes in the oceanic thermal structure. Sensitivity experiments considering future warming climates are conducted using a fully coupled air–sea numerical model. An average increase in sea surface temperature (SST) by 4.53°C can lead to a reduction in the minimum sea level pressure by 19.4 hPa. Ocean warming enhances oceanic mixing, potentially increasing the availability of heat energy for typhoon's development (i.e., an average increase in SST by 4.53°C leads to a 15.30% increase in heat energy). However, future changes in atmospheric temperature and humidity will moderate the intensification of typhoons induced by ocean warming. These results are expected to provide information for assessing the future changes in typhoon intensity under a warming climate, which is important for predicting and reducing sequential risks. [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]