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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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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

El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement: The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem. [ABSTRACT FROM AUTHOR]

The impacts of tropical sea surface temperature (SST) changes on the Madden-Julian oscillation (MJO) are investigated using the large-ensemble simulation from the Community Earth System Model version 2 (CESM2-LE) under the shared socioeconomic pathway (SSP370 scenario). Three SST change patterns are featured, distinguished by the zonal gradient of the change in the equatorial Pacific warming. MJO characteristics and its teleconnections responses are composited for the clusters, and their relationships to the zonal SST gradient changes are examined. Results show that the anomalously strong El Niño-like SST change pattern significantly intensifies the MJO amplitude and enhances its eastward extension compared to the anomalously weak El Niño-like SST change pattern. These changes in MJO amplitude are further interpreted through the α framework. We also found no statistically different extratropical geopotential height responses to MJO between the three SST warming patterns, possibly due to strong internal climate variability. Changes in Rossby wave source between clusters also show a weak relationship with the MJO teleconnections. Our results highlight the importance of Indo-Pacific zonal SST gradient changes on the changes of MJO but limited impacts on MJO teleconnections to the midlatitudes. [ABSTRACT FROM AUTHOR]

Ongoing degradation of the Congolese rain forest is documented, but the individual roles of climate change and deforestation are unknown. A modified version of the Centro de Previsao de Tempo e Estudios Climaticos (CPTEC) potential vegetation model (PVM) forced by ERA5 reanalysis data translates decadal climate states (1980–2020) into natural vegetation distributions to identify regions where climate change could have played a role in changing vegetation. These areas are then examined to understand how and why these climate changes could affect the tropical rain forest coverage. Between the 1980s and the 2010s, the climate over the northern and southern Congo basin rain forest margins becomes less able to support the forest. In the north, strong, negative meridional moisture gradients in boreal winter separate warm, dry conditions to the north from the cooler, moist rain forest. By the 2010s greenhouse gas warming deepens the low-level trough in the north, enhancing the inflow of drier subtropical air. A similar drying response occurs over the southern margin during austral winter when the low-level westerly transport of Atlantic moisture decreases in association with warming and reduced low-level heights over the equatorial Congo basin. In the interior, climate conditions also become less favorable along major transportation routes by the 2010s due to human intervention/deforestation. Along coastal Angola, the climate becomes more favorable for tropical forest vegetation when coastal upwelling weakens and SSTs warm in response to changes in the South Atlantic subtropical anticyclone. These results have implications for the future as global warming continues. [ABSTRACT FROM AUTHOR]

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

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

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

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December-February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season's rainfall over the period 1500-2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño-Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance. [ABSTRACT FROM AUTHOR]

This paper examines the physical processes controlling how synoptic midlatitude temperature variability near the surface changes with climate. Because synoptic temperature variability is primarily generated by advection, it can be related to mean potential temperature gradients and mixing lengths near the surface. Scaling arguments show that the reduction of meridional potential temperature gradients that accompanies polar amplification of global warming leads to a reduction of the synoptic temperature variance near the surface. This is confirmed in simulations of a wide range of climates with an idealized GCM. In comprehensive climate simulations (CMIP5), Arctic amplification of global warming similarly entails a large-scale reduction of the near-surface temperature variance in Northern Hemisphere midlatitudes, especially in winter. The probability density functions of synoptic near-surface temperature variations in midlatitudes are statistically indistinguishable from Gaussian, both in reanalysis data and in a range of climates simulated with idealized and comprehensive GCMs. This indicates that changes in mean values and variances suffice to account for changes even in extreme synoptic temperature variations. Taken together, the results indicate that Arctic amplification of global warming leads to even less frequent cold outbreaks in Northern Hemisphere winter than a shift toward a warmer mean climate implies by itself. [ABSTRACT FROM AUTHOR]

Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections. [ABSTRACT FROM AUTHOR]

Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing general circulation models (GCMs) and last millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multidecadal and longer time scales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: 1) degrees of freedom (;N) and 2) spatial coherence. First, we use N and spatial coherence to characterize variability across a suite of both preindustrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewer N and higher coherence between local and global mean surface temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth's energy budget and downgradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCMdifferences. [ABSTRACT FROM AUTHOR]

Atmospheric angular momentum (AAM) is used to study the variability of Earth's atmospheric circulation during the past 45 years, a time of considerable climate change. Using global AAM, two interdecadal states are defined covering the periods 1977–98 (hereinafter P1) and 1999–2022 (P2). Global AAM decreased from P1 to P2 and was accompanied by weakened subtropical jet streams in both hemispheres, strong convection around the northern Maritime Continent, and a strengthened sea surface temperature (SST) gradient across the tropical Pacific Ocean. The period differences project onto 1) internal interdecadal Pacific variability (IPV), 2) a postulated transient ocean thermostat response to greenhouse gas and aerosol emissions, and 3) circulation anomalies related to the ozone hole. During 1977–2023, the first two processes are forcing the climate toward larger Pacific Ocean SST gradients and a poleward expansion of the Indo-Pacific warm pool (IPWP), especially into the Northern Hemisphere. The ozone hole produces its own distinct pattern of anomalies in the Southern Hemisphere that tend to become persistent in the early 1990s. The zonal and vertical mean AAM variations during P1 have frequent westerly wind anomalies between 40°N and 40°S with poleward propagation on interannual time scales. During P2, the circulation is dominated by subtropical easterly wind anomalies, poleward-shifted jets, and weaker propagation. Locally, the zonal mean anomalies manifest as midlatitude ridges that lead to continental droughts. Case studies illustrate the weakened subtropical jet streams of P2 and examine the factors behind a transition to La Niña in early 2020 that maintains the P2 pattern. [ABSTRACT FROM AUTHOR]

Oxygen isotope speleothems have been widely used to infer past climate changes over tropical South America (TSA). However, the spatial patterns of the millennial precipitation and precipitation δ18O (δ18Op) response have remained controversial, and their response mechanisms are unclear. In particular, it is not clear whether the regional precipitation represents the intensity of the millennial South American summer monsoon (SASM). Here, we study the TSA hydroclimate variability during the last deglaciation (20–11 ka ago) by combining transient simulations of an isotope-enabled Community Earth System Model (iCESM) and the speleothem records over the lowland TSA. Our model reasonably simulates the deglacial evolution of hydroclimate variables and water isotopes over the TSA, albeit underestimating the amplitude of variability. North Atlantic meltwater discharge is the leading factor driving the TSA’s millennial hydroclimate variability. The spatial pattern of both precipitation and δ18Op show a northwest–southeast dipole associated with the meridional migration of the intertropical convergence zone, instead of a continental-wide coherent change as inferred in many previous works on speleothem records. The dipole response is supported by multisource paleoclimate proxies. In response to increased meltwater forcing, the SASM weakened (characterized by a decreased low-level easterly wind) and consequently reduced rainfall in the western Amazon and increased rainfall in eastern Brazil. A similar dipole response is also generated by insolation, ice sheets, and greenhouse gases, suggesting an inherent stability of the spatial characteristics of the SASM regardless of the external forcing and time scales. Finally, we discuss the potential reasons for the model–proxy discrepancy and pose the necessity to build more paleoclimate proxy data in central-western Amazon. Significance Statement We want to reconcile the controversy on whether there is a coherent or heterogeneous response in millennial hydroclimate over tropical South America and to clearly understand the forcing mechanisms behind it. Our isotope-enabled transient simulations fill the gap in speleothem reconstructions to capture a complete picture of millennial precipitation/δ18Op and monsoon intensity change. We highlight a heterogeneous dipole response in precipitation and δ18Op on millennial and orbital time scales. Increased meltwater discharge shifts ITCZ southward and favors a wet condition in coastal Brazil. Meanwhile, the low-level easterly and the summer monsoon intensity reduced, causing a dry condition in the central-western Amazon. However, the millennial variability of hydroclimate response is underestimated in our model, together with the lack of direct paleoclimate proxies in the central-west Amazon, complicating the interpretation of changes in specific paleoclimate events and posing a challenge to constraining the spatial range of the dipole. Therefore, we emphasize the necessity to increase the source of proxies, enhance proxy interpretations, and improve climate model performance in the future. [ABSTRACT FROM AUTHOR]

Unstable hydrological cycles and water resource instability over and around the Tibetan Plateau (TP) are topics of wide concern. The Indian summer monsoon (ISM) is one of the TP's most important moisture sources; as such, its behavior is key to any changes in precipitation and water-related environments. However, there have been relatively few thorough investigations into ISM activities. Here we primarily explore ISM activities using outgoing longwave radiation (OLR) datasets in TP, and precipitation isotopes recorded at Lhasa, for the period 1975–2020. Our major findings are that 1) the ISM onset (retreat date) is between ∼31 May and 19 July (∼8 August–27 September), with ISM duration of ∼40–110 days; 2) significant spatial inhomogeneous patterns are evident in ISM activities, i.e., the western part of our study area experiences earlier ISM onset, delayed retreat, longer duration, and greater intensity and strength, and the inverse is true in the eastern sector of the study area; 3) the ISM activities that dominate the 1975–98 period determine their general patterns over the entire 1975–2020 period, taking into account evident discrepancies in subperiods; and 4) the negative relations between precipitation δ18O and ISM intensity/strength at Lhasa confirm the ISM activities defined using OLR. These results will improve our understanding of hydrological cycles in TP and provide insights into hydrological studies in the "Asian Water Tower" region. Significance Statement: Over the recent decades, the Tibetan Plateau (TP) (Asian Water Tower) has undergone dramatic environmental changes, evinced by the instabilities of hydrological cycles. As one of TP's most important moisture sources, the Indian summer monsoon (ISM) is key to changes in precipitation and water-related environments. To get thorough investigations into ISM activities, we primarily explore ISM activities using outgoing longwave radiation (OLR) datasets in TP and precipitation isotopes at Lhasa. Significant spatial inhomogeneous patterns are evident in ISM activities: the western part experiences earlier ISM onset, delayed retreat, longer duration, and stronger intensity and strength, and the inverse occurs in the eastern sector. These results will improve our understanding of hydrology, meteorology, ecology, and paleoclimate reconstructions in the Asian Water Tower region. [ABSTRACT FROM AUTHOR]

Persistent warming and water cycle change due to anthropogenic climate change modifies the temperature and salinity distribution of the ocean over time. This "forced" signal of temperature and salinity change is often masked by the background internal variability of the climate system. Analyzing temperature and salinity change in water-mass-based coordinate systems has been proposed as an alternative to traditional Eulerian (e.g., fixed-depth, zonally averaged) coordinate systems. The impact of internal variability is thought to be reduced in water-mass coordinates, enabling a cleaner separation of the forced signal from background variability}or a higher "signal-to-noise" ratio. Building on previous analyses comparing Eulerian and water-mass-based one-dimensional coordinates, here we recast two-dimensional coordinate systems-temperature--salinity (T-S), latitude--longitude, and latitude--depth}onto a directly comparable equal-volume framework. We compare the internal variability, or "noise" in temperature and salinity between these remapped two-dimensional coordinate systems in a 500-yr preindustrial control run from a CMIP6 climate model. We find that the median internal variability is lowest (and roughly equivalent) in T-S and latitude--depth space, compared with latitude--longitude coordinates. A large proportion of variability in T-S and latitude--depth space can be attributed to processes that operate over a time scale greater than 10 years. Overall, the signal-to-noise ratio in T--S coordinates is roughly comparable to latitude--depth coordinates, but is greater in regions of high historical temperature change. Conversely, latitude--depth coordinates have greater signal-to-noise ratio in regions of historical salinity change. Thus, we conclude that the climatic temperature change signal can be more robustly identified in water-mass coordinates. [ABSTRACT FROM AUTHOR]

The atmospheric circulation response to global warming is an important problem that is theoretically still not well understood. This is a particular issue since climate model simulations provide uncertain, and at times contradictory, projections of future climate. In particular, it is still unclear how a warmer and moister atmosphere will affect midlatitude eddies and their associated poleward transport of heat and moisture. Here we perform a trend analysis of three main components of the global circulation}the zonal-mean state, eddies, and the net energy input into the atmosphere}and examine how they relate in terms of a moist static energy budget for the JRA-55 reanalysis data. A particular emphasis is made on understanding the contribution of moisture to circulation trends. The observed trends are very different between the hemispheres. In the Southern Hemisphere there is an overall strengthening and during boreal summer, also a poleward shifting, of the jet stream, the eddies, and the meridional diabatic heating gradients. Correspondingly, we find an overall strengthening of the meridional gradients of the net atmospheric energy input. In the Northern Hemisphere, the trend patterns are more complex, with the dominant signal being a clear boreal winter Arctic amplification of positive trends in lower-tropospheric temperature and moisture, as well as a significant weakening of both bandpass and low-pass eddy heat and moisture fluxes. Consistently, surface latent and sensible heat fluxes, upward and downward longwave radiation, and longwave cloud radiative fluxes at high latitudes show significant trends. However, radiative fluxes and eddy fluxes are inconsistent, suggesting data assimilation procedures need to be improved. [ABSTRACT FROM AUTHOR]

We develop a statistical method to assess CMIP6 simulations of large-scale surface temperature change during the historical period (1850–2014), considering all time scales, allowing for the different unforced variability of each model and the observations, observational uncertainty, and variable ensemble size. The generality of this method, and the fact that it incorporates information about the unforced variability, makes it a useful model assessment tool. We apply this method to the historical simulations of the CMIP6 multimodel ensemble. We use three indices that measure different aspects of large-scale surface air temperature change: global mean, hemispheric gradient, and a recently developed index that captures the sea surface temperature (SST) pattern in the tropics (SST#; see Fueglistaler and Silvers). We use the following observations: HadCRUT5 for the first two indices, and AMIPII and ERSSTv5 for SST#. In each case, we test the hypothesis that the model's forced response is compatible with the observations, accounting for unforced variability in both models and observations as well as measurement uncertainty. This hypothesis is accepted more often (75% of the models) for the hemispheric gradient than for the global mean, for which half of the models fail the test. The tropical SST pattern is poorly simulated in all models. Given that the tropical SST pattern can strongly modulate the relationship between energy imbalance and global-mean surface temperature anomalies on annual to decadal time scales (short-term feedback parameter), we suggest this should be a focus area for future improvements due to its potential implications for the global-mean temperature evolution in decadal time scales. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) is an important but not the only source of interannual variability over the Indo–western Pacific. Non-ENSO forced variability in the region has received recent attention because of the implications for rainy-season prediction. Using a 35-member CESM1 Large Ensemble (CESM-LE) and 30 CMIP6 models, this study shows that the ensemble means project intensified interannual variability for precipitation, low-level winds, and sea level pressure under global warming, associated with the enhanced large-scale anomalous anticyclone (AAC) over the tropical northwestern (NW) Pacific after the ENSO signal is removed. A decomposition based on the column water vapor budget reveals that enhanced precipitation variability is due to the increased background specific humidity. The resultant anomalous diabatic heating intensifies the AAC, which further strengthens the precipitation anomalies. Over the tropical NW Pacific, the wind-induced evaporative cooling on the southeastern flank of the AAC is countered by the increased shortwave radiation due to the strengthened precipitation reduction. Tropospheric temperature anomalies in the ensemble means show no significant change, suggesting no apparent change of the interbasin positive feedback between the AAC and northern Indian Ocean SST. Intermodel analysis based on CMIP6 reveals that models with a larger increase in ENSO-unrelated precipitation variability over the NW Pacific are associated with stronger background warming in the eastern equatorial Pacific, due to the modulated Walker and Hadley circulations. [ABSTRACT FROM AUTHOR]

The persistence barrier (PB), one of the El Niño–Southern Oscillation (ENSO) properties, has exhibited a significant decadal variability, showing enhanced and weakened behavior before and after the late 1970s, respectively. In the present study, both the theoretical solution and the observations indicate that the variability of PB intensity is linearly proportional to the seasonal amplitude of ENSO growth rate, which accounts for the ENSO PB decadal variability. With further use of the Bjerknes–Jin (BJ) index analysis, we find that the decadal reduction in PB intensity since the late 1970s is mainly attributed to the mean advection and the thermocline feedback. In addition, the stronger spring thermal damping delayed the timing of PB in the 1980s and 1990s. Our study establishes a linear relationship between PB intensity and ENSO growth rate, which carries implications for understanding the ENSO predictability and the systematic changes in ENSO properties under climate change. [ABSTRACT FROM AUTHOR]

Changes in the background climate are known to affect El Niño–Southern Oscillation (ENSO) by altering feedbacks that control ENSO's characteristics. Here, the sensitivity of ENSO variability to the background climate is investigated by utilizing two Community Earth System Model, version 1 (CESM1), simulations in which the solar constant is altered by ±25 W m−2. The resulting stable warm and cold climate mean state simulations differ in terms of ENSO amplitude, frequency, diversity, asymmetry, and seasonality. In the warm run, ENSO reveals a larger amplitude and occurs at higher frequencies relative to the cold and control runs as well as observations. The warm run also features more eastern Pacific El Niños, an increased asymmetry, and a stronger seasonal phase locking. These changes are linked to changes in the mean state via the amplifying and damping feedbacks. In the warm run, a shallower mean thermocline results in a stronger subsurface–surface coupling, whereas the cold run reveals reduced ENSO variability due to a reduced Bjerknes feedback in accordance with a deeper mean thermocline and enhanced surface wind stress. A strong zonal advective and upwelling feedback further contribute to the large ENSO amplitude in the run with a warmer mean state. In the cold run, ENSO events are partly forced by anomalous shortwave radiation. However, in light of the large temperature contrast between the simulations of up to 6 K in the tropical Pacific, the relatively small changes in ENSO variability highlight the robustness of ENSO dynamics under vastly different climate mean states. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) translation distance, proportional to its duration and translation speed, basically determines the spatial extent of TC-impacted marine and terrestrial areas. Although a long-term slowdown of TCs has recently been reported, changes in translation distance of TCs over the western North Pacific (WNP) and their driving mechanisms remain poorly understood. Using multiple TC datasets, here we find that the trends are opposite in overland translation distance of landfall WNP TCs over China and excluding China, with the number of landfalls in the two groups being almost the same. However, the increase in overland translation distance of landfall TCs over China is offset by a greater decrease in that excluding China, with the result that the overland translation distance of TCs over the WNP has declined (−4.0% decade−1). The oversea translation distance of TCs over the WNP also shows a decreasing trend (−2.4% decade−1), and thus the genesis-to-demise translation distance of TCs over the WNP has significantly decreased (by 17.7%) during 1961–2019. The shorter duration and reduced translation speed of TCs directly and jointly contribute (by 76.9% and 23.1%, respectively) to their shortened translation distance. The gradual increase in westerly and northerly winds and weaker moisture transport during the days following TC generation tend to inhibit long-distance travel after TC genesis. Declining TC translation distance is likely associated with the long-term increases in genesis potential index and an anomalous cyclonic steering flow over the WNP. Our findings suggest that these changes in TC translation distance may alter regional patterns of TC-related drying and wetting of the WNP. Significance Statement: The spatial extent affected by a tropical cyclone (TC) is largely determined by TC translation distance. For example, TCs passing through marine areas could cool sea surface temperature and modulate local air–sea heat fluxes exchange; TCs with longer translation distances over land are more likely to trigger widespread flooding or terminate droughts in inland areas. Over the western North Pacific (WNP), we find that observational evidence supports opposite trends in overland translation distance of landfall TCs over China and excluding China. Nevertheless, the genesis-to-demise translation distance of TCs shows a significant decline over the WNP during the past six decades. Understanding the impacts of changes in TC translation distance has particular importance given the extensive footprint of TC-related drying and wetting of the marine and terrestrial areas. [ABSTRACT FROM AUTHOR]

Warming of the climate system accumulates mostly in the ocean and discrepancies in how this is modeled contribute to uncertainties in predicting sea level rise. In this study, regional temperature changes in an atmosphere–ocean general circulation model (HadCM3) are partitioned between excess (due to perturbed surface heat fluxes) and redistributed (arising from changing circulation and perturbations to mixing) components. In simulations with historical forcing, we first compare this excess–redistribution partitioning with the spice and heave decomposition, in which temperature anomalies enter the ocean interior either along isopycnals (spice) or across isopycnals (heave, without affecting the temperature– salinity curve). Second, heat and salinity budgets projected into thermohaline space naturally reveal the mechanisms behind temperature change by spice and heave linked with water mass generation or destruction. Excess warming enters the ocean as warming by heave in subtropical gyres whereas it mainly projects onto warming by spice in the Southern Ocean and the tropical Atlantic. In subtropical gyres, Ekman pumping generates excess warming as confirmed by Eulerian heat budgets. In contrast, isopycnal mixing partly drives warming and salinification by spice, as confirmed by budgets in thermohaline space, underlying the key role of salinity changes for the ocean warming signature. Our study suggests a method to detect excess warming using spice and heave calculated from observed repeat profiles of temperature and salinity. [ABSTRACT FROM AUTHOR]

Summer mean (June, July, and August) surface air temperature (SSAT) in East Asia during the period from 1958 to 2001 has shown a warming. However, the relative contributions of external forcing (EF) and internal climate variability (ICV) to the SSAT changes over East Asia remain unclear. In this study, a new approach is applied to estimate the changes in the SSAT determined by the effects of EF and ICV over East Asia during the period from 1958 to 2001. Reanalysis data as well as simulated results from both global atmosphere–ocean coupled model outputs and a regional climate model (RCM) are used for this approach. The observed SSATs over East Asia have undergone a decreasing trend from 1958 to 1972 (20.148C decade-1, p<0.01) and an increasing trend after 1972 (0.248C decade-1, p<0.01). While these features are not captured by the reanalysis studied here, they are reproduced when the reanalysis output is downscaled using an RCM. The effects of the EF and the ICV on the SSAT can be separated based on the RCM downscaling simulation. The results show that the SSAT with EF displayed significant warming over most regions of East Asia, whereas the SSAT with ICV mainly exhibited cooling over East Asia. Furthermore, EF mainly influenced the decadal changes of the SSAT, whereas the ICV mainly influenced the interannual changes in the SSAT over East Asia. The interannual changes of the SSAT over East Asia that were influenced by the ICV are mainly manifested as the combined effects of the large-scale ocean–atmosphere circulations, which expressed 79% explanatory power on the SSAT changes. [ABSTRACT FROM AUTHOR]

Precipitation in China is controlled by a combination of multiple weather systems and is susceptible to their spatiotemporal variability under climate warming. Based on the daily precipitation dataset developed by the China Meteorological Administration and ERA5 6-hourly data from 1979 to 2018, we detected tropical cyclones, extratropical cyclones, and fronts affecting mainland China of an altitude less than 1500 m using objective methods and identified their individually associated precipitation. We quantified climatological characteristics of precipitation associated with these three meteorological causes and their proportion of precipitation totals, and evaluated their relative contribution to change in precipitation. Results show that precipitation associated with tropical cyclones (TCP), extratropical cyclones (ETCP), and fronts (FRTP) accounted for 3.0%, 21.2%, and 20.4% of the total precipitation in China, and constituted the largest percentage in August, June, and January, respectively. The total proportion of these three types of precipitation exceeded 50% in more than 55% of China's regions. The high-value regions of corresponding precipitation were directly related to the locations of frequent occurrences of these weather systems. TCP and FRTP increased over the past four decades, while ETCP decreased. The western part of Northeast China and the middle and upper reaches of the Yangtze River show precipitation decreases, 37.8% of which is contributed by ETCP decreases. Precipitation increased in most regions of China during the dry season, 34.1% of which is contributed by FRTP increases. Among them, Northeast (Northwest) China significantly increased it precipitation by 14.4% decade−1 (21.8% decade−1), 33.6% (51.3%) of which is contributed by FRTP increases. [ABSTRACT FROM AUTHOR]

Unlike greenhouse gases (GHGs), anthropogenic aerosol (AA) concentrations have increased and then decreased over the past century or so, with the timing of the peak concentration varying in different regions. To date, it has been challenging to separate the climate impact of AAs from that due to GHGs and background internal variability. We use a pattern recognition method, taking advantage of spatiotemporal covariance information, to isolate the forced patterns for the surface ocean and associated atmospheric variables from the all-but-one forcing Community Earth System Model ensembles. We find that the aerosol-forced responses are dominated by two leading modes, with one associated with the historical increase and future decrease of global mean aerosol concentrations (dominated by the Northern Hemisphere sources) and the other due to the transition of the primary sources of AA from the west to the east and also from Northern Hemisphere extratropical regions to tropical regions. In particular, the aerosol transition effect, to some extent compensating the global mean effect, exhibits a zonal asymmetry in the surface temperature and salinity responses. We also show that this transition effect dominates the total AA effect during recent decades, e.g., 1967–2007. [ABSTRACT FROM AUTHOR]

The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model's SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022. [ABSTRACT FROM AUTHOR]

The authors argue that the concept of snapshot attractors and of their natural probability distributions are the only available tools by means of which mathematically sound statements can be made about averages, variances, etc., for a given time instant in a changing climate. A basic advantage of the snapshot approach, which relies on the use of an ensemble, is that the natural distribution and thus any statistics based on it are independent of the particular ensemble used, provided it is initiated in the past earlier than a convergence time. To illustrate these concepts, a tutorial presentation is given within the framework of a low-order model in which the temperature contrast parameter over a hemisphere decreases linearly in time. Furthermore, the averages and variances obtained from the snapshot attractor approach are demonstrated to strongly differ from the traditional 30-yr temporal averages and variances taken along single realizations. The authors also claim that internal variability can be quantified by the natural distribution since it characterizes the chaotic motion represented by the snapshot attractor. This experience suggests that snapshot-attractor-based calculations might be appropriate to be evaluated in any large-scale climate model, and that the application of 30-yr temporal averages taken along single realizations should be complemented with this more appealing tool for the characterization of climate changes, which seems to be practically feasible with moderate ensemble sizes. [ABSTRACT FROM AUTHOR]

A reconstruction of Antarctic monthly mean near-surface temperatures spanning 1958-2012 is presented. Its primary goal is to take advantage of a recently revised key temperature record from West Antarctica (Byrd) to shed further light on multidecadal temperature changes in this region. The spatial interpolation relies on a kriging technique aided by spatiotemporal temperature covariances derived from three global reanalyses [the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR)]. For 1958-2012, the reconstruction yields statistically significant annual warming in the Antarctic Peninsula and virtually all of West Antarctica, but no significant temperature change in East Antarctica. Importantly, the warming is of comparable magnitude both in central West Antarctica and in most of the peninsula, rather than concentrated either in one or the other region as previous reconstructions have suggested. The Transantarctic Mountains act for the temperature trends, as a clear dividing line between East and West Antarctica, reflecting the topographic constraint on warm air advection from the Amundsen Sea basin. The reconstruction also serves to highlight spurious changes in the 1979-2009 time series of the three reanalyses that reduces the reliability of their trends, illustrating a long-standing issue in high southern latitudes. The study concludes with an examination of the influence of the southern annular mode (SAM) on Antarctic temperature trends. The results herein suggest that the trend of the SAM toward its positive phase in austral summer and fall since the 1950s has had a statistically significant cooling effect not only in East Antarctica (as already well documented) and but also (only in fall) in West Antarctica. [ABSTRACT FROM AUTHOR]

Understanding variability of the Indonesian Throughflow (ITF) and its links to El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD), and how they are represented across climate models constitutes an important step toward improved future climate projections. These issues are examined using 20 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the SODA-2.2.4 ocean reanalysis. It is found that the CMIP5 models overall simulate aspects of ITF variability, such as spectral and vertical structure, that are consistent with the reanalysis, although intermodel differences are substantial. The ITF variability is shown to exhibit two dominant principal vertical structures: a surface-intensified transport anomaly (ITFM1) and an anomalous transport characterized by opposing flows in the surface and subsurface (ITF2M). In the CMIP5 models and reanalysis, ITF2M is linked to both ENSO and the IOD via anomalous Indo-Pacific Walker circulation. The driver of ITFM1 however differs between the reanalysis and the CMIP5 models. In the reanalysis ITFM1 is a delayed response to ENSO, whereas in the CMIP5 models it is linked to the IOD associated with the overly strong IOD amplitude bias. Further, the CMIP5 ITF variability tends to be weaker than in the reanalysis, due to a tendency for the CMIP5 models to simulate a delayed IOD in response to ENSO. The importance in considering the vertical structure of ITF variability in understanding ENSO and IOD impact is further underscored by the close link between greenhouse-forced changes in ENSO variability and projected changes in subsurface ITF variability. [ABSTRACT FROM AUTHOR]

The latest Coupled Model Intercomparison Project (CMIP6) multimodel ensemble shows a broader range of projected warming than the previous-generation CMIP5 ensemble. We show that the projected warming is well correlated with tropical and subtropical low-level cloud properties. These physically meaningful relations enable us to use observed cloud properties to constrain future climate warming. We develop multivariate linear regression models with metrics selected from a set of potential constraints based on a stepwise selection approach. The resulting linear regression model using two low-cloud metrics shows better cross-validated results than regression models that use single metrics as constraints. Application of a regression model using the low-cloud metrics to climate projections results in similar estimates of the mean, but substantially narrower uncertainty ranges, of projected twenty-first-century warming when compared with unconstrained simulations. The resulting projected global-mean warming in 2081–2100 relative to 1995–2014 is 2.84–5.12 K (5%–95% range) for Shared Socioeconomic Pathway (SSP) 5–8.5 compared with a range of 2.34–5.81 K for unconstrained projections, and 0.60–1.70 K for SSP1–2.6 compared to an unconstrained range of 0.38–2.04 K. We provide evidence for a higher lower bound of the projected warming range than that obtained from constrained projections based on the past global-mean temperature trend. Consideration of the impact of the sea surface temperature pattern effect on the recent observed warming trend, which is not well captured in the CMIP6 ensemble, indicates that the relatively low projected warming resulting from the global-mean temperature trend constraint may not be reliable and provides further justification for the use of climatologically based cloud metrics to constrain projections. [ABSTRACT FROM AUTHOR]

There are heated debates on the existence of the global warming slowdown during the early twenty-first century. Although efforts have been made to clarify or reconcile the controversy over this issue, it is not explicitly addressed, restricting the understanding of global temperature change particularly under the background of increasing greenhouse gas concentrations. Here, using extensive temperature datasets, we comprehensively reexamine the existence of the slowdown under all existing definitions during all decadal-scale periods spanning 1990–2017. Results show that the short-term linear trend–dependent definitions of slowdown make its identification severely suffer from the period selection bias, which largely explains the controversy over its existence. Also, the controversy is further aggravated by the significant impacts of the differences between various datasets on the recent temperature trend and the different baselines for measuring slowdown prescribed by various definitions. However, when the focus is shifted from specific periods to the probability of slowdown events, we find the probability is significantly higher in the 2000s than in the 1990s, regardless of which definition and dataset are adopted. This supports a slowdown during the early twenty-first century relative to the warming surge in the late twentieth century, despite higher greenhouse gas concentrations. Furthermore, we demonstrate that this decadal-scale slowdown is not incompatible with the centennial-scale anthropogenic warming trend, which has been accelerating since 1850 and never pauses or slows. This work partly reconciles the controversy over the existence of the warming slowdown and the discrepancy between the slowdown and anthropogenic warming. [ABSTRACT FROM AUTHOR]

Simultaneous heatwaves affecting multiple regions (referred to as concurrent heatwaves) pose compounding threats to various natural and societal systems, including global food chains, emergency response systems, and reinsurance industries. While anthropogenic climate change is increasing heatwave risks across most regions, the interactions between warming and circulation changes that yield concurrent heatwaves remain understudied. Here, we quantify historical (1979–2019) trends in concurrent heatwaves during the warm season [May–September (MJJAS)] across the Northern Hemisphere mid- to high latitudes. We find a significant increase of ∼46% in the mean spatial extent of concurrent heatwaves and ∼17% increase in their maximum intensity, and an approximately sixfold increase in their frequency. Using self-organizing maps, we identify large-scale circulation patterns (300 hPa) associated with specific concurrent heatwave configurations across Northern Hemisphere regions. We show that observed changes in the frequency of specific circulation patterns preferentially increase the risk of concurrent heatwaves across particular regions. Patterns linking concurrent heatwaves across eastern North America, eastern and northern Europe, parts of Asia, and the Barents and Kara Seas show the largest increases in frequency (∼5.9 additional days per decade). We also quantify the relative contributions of circulation pattern changes and warming to overall observed concurrent heatwave day frequency trends. While warming has a predominant and positive influence on increasing concurrent heatwave frequency, circulation pattern changes have a varying influence and account for up to 0.8 additional concurrent heatwave days per decade. Identifying regions with an elevated risk of concurrent heatwaves and understanding their drivers is indispensable for evaluating projected climate risks on interconnected societal systems and fostering regional preparedness in a changing climate. Significance Statement: Heatwaves pose a major threat to human health, ecosystems, and human systems. Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages. We assess recent changes in the occurrence of simultaneous large heatwaves. Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia. Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts. [ABSTRACT FROM AUTHOR]

The subtropical Indian Ocean dipole (SIOD) and Ningaloo Niño are the two dominant modes of interannual climate variability in the subtropical south Indian Ocean. Observations show that the SIOD has been weakening in the recent decades, while Ningaloo Niño has been strengthening. In this study, we investigate the causes for such changes by analyzing climate model experiments using the NCAR Community Earth System Model, version 1 (CESM1). Ensemble-mean results from CESM1 large-ensemble (CESM1-LE) show that the external forcing causes negligible changes in the amplitudes of the SIOD and Ningaloo Niño, suggesting a dominant role of internal climate variability. Meanwhile, results from CESM1 pacemaker experiments reveal that the observed changes in the two climate modes cannot be attributed to the effect of sea surface temperature anomalies (SSTA) in either the eastern tropical Pacific Ocean or tropical Indian Ocean. By further comparing different ensemble members from the CESM1-LE, we find that a warm pool dipole mode of decadal variability, with opposite SSTA in the southeast Indian Ocean and the western-central tropical Pacific Ocean plays an important role in driving the observed changes in the SIOD and Ningaloo Niño. These changes in the two climate modes have considerable impacts on precipitation and sea level variabilities in the south Indian Ocean region. [ABSTRACT FROM AUTHOR]

The approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of "what could have occurred" given the same climate system and boundary conditions but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the upper Colorado River basin: frequency of dry, wet, and whiplash years. Thirty-one-year "climatologies" in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of internal variability alone. [ABSTRACT FROM AUTHOR]

In this study, the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian–Pacific–American region is examined in 26 CMIP5/6 model outputs. Compared to observations, most models fail to simulate the correct ENSO–STV relationship in historical simulations. To investigate the possible bias in the ENSO–STV simulations, two possible processes for the connection between ENSO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. On the one hand, both HPS and LPS models can overall reproduce a reasonable relationship between STV and the mean-flow conditions supporting extratropical eddy development. On the other hand, only HPS models can well capture the relationship between ENSO and the development of extratropical eddies, while LPS models fail to simulate this feature, indicating that the bias in the simulated ENSO–STV relationship among CMIP5/6 models can be traced back to ENSO simulation. Furthermore, the bias of the ENSO simulation is characterized by an unreasonable SST pattern bias, with an excessive westward extension of warm SST anomalies over the western Pacific and weak warm SST anomalies over the equatorial central-eastern Pacific, resulting in the underestimation of the zonal SST anomaly gradient among models. Therefore, the ENSO pattern bias induces an unrealistic circulation and temperature gradient over the Asian–Pacific–American region, affecting the simulations of the ENSO–STV connection. In addition, the ENSO–STV relationship over the Asian–Pacific–American region is still robust in future projections based on HPS models, providing implications for the selection of future climate predictors. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of models from phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing feedbacks in models with those inferred from observations. Overall, we find no systematic disagreements between the feedbacks in the model ensembles and feedbacks inferred from observations, although there is a wide range in the ability of individual models to reproduce the observations. In particular, 40 of 52 models have best estimates that fall within the uncertainty of the observed total feedback. We quantify two sources of uncertainty in the model ensembles: 1) the structural difference, due to the differences in model parameterizations, and 2) the unforced pattern effect, due to unforced variability, and find that both are important when comparing with an 18-yr observational dataset. We perform the comparison using two energy balance frameworks: the traditional energy balance framework, in which it is assumed that changes in energy balance are controlled by changes in global average surface temperatures, and an alternative framework that assumes the changes in energy balance are controlled by tropical atmospheric temperatures. We find that the alternative framework provides a more robust way of comparing the models with observations, with both smaller structural differences and smaller unforced pattern effect. However, when considering the relation of feedbacks in response to interannual variability and long-term warming, the traditional framework has advantages. There are no great differences between the CMIP5 and CMIP6 ensembles' ability to reproduce the observed feedbacks. [ABSTRACT FROM AUTHOR]

To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980–2018) for 4722 meteorological stations. Through examining monthly averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (−19%), South America (−16%), Australia (−14%), and Asia (−13%), with corresponding rate reductions of −0.13, −0.08, −0.09, and −0.06 m s−1 decade−1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean–atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including the Western Hemisphere warm pool, East Atlantic pattern, Pacific decadal oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind power generation, now and in the future. [ABSTRACT FROM AUTHOR]

The western Pacific pattern (WP) is one of the most important atmospheric teleconnections over the Northern Hemisphere (NH) in boreal winter and plays key roles in regulating weather and climate variations over many parts of the NH. This study evaluates the ability of the coupled models participating in CMIP5 and CMIP6 to capture the spatial pattern, dominant frequency, and associated climate anomalies of the winter WP. Ensemble means of the CMIP5 and CMIP6 models well capture spatial structures of the WP, with slightly higher skills for the CMIP6. However, the northern (southern) center of the WP is shifted westward (eastward) relative to the observations, and the strength of the northern center is overestimated in most CMIP5 and CMIP6 models. CMIP6 shows an improvement in simulating the dominant periodicity of the WP. WP-related climatic anomalies in most parts of the NH can be well simulated. However, there exists a large spread across the models in simulating surface air temperature (SAT) anomalies in the Russian Far East and northwest North America, which is attributable to the diversity of the intensity of the WP's northern lobe. Most CMIP5 and CMIP6 models largely overestimate the WP-related precipitation anomalies over Siberia, which is partly due to the overestimation of mean precipitation there. Furthermore, most models simulate a close relation of the WP and Arctic Oscillation (AO), which does not exist in observation. The CMIP5 and CMIP6 models with weak WP–AO relations have better ability than the models with strong WP–AO relations in capturing the WP-related SAT and precipitation anomalies over the NH, especially over Eurasia. [ABSTRACT FROM AUTHOR]