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Climate change has forced the world into a state of emergency, but the urgency can also become an opportunity to strengthen the focus on sustainable development and reduce social vulnerability. For developing economies, the first and foremost challenge regarding climate change is to address the knowledge gap on sustainable development and vulnerability. Besides this, evidence-based inputs are needed for the policies and programs that intend to enhance the adaptive capacity and social capital from the gender perspective in comparatively more disaster-prone districts of the country. The environmental impact in terms of socioeconomic conditions specifically pertaining to rural areas of Pakistan cannot be ignored. Natural events such as floods and droughts have raised the question of the social and socioeconomic vulnerability of the rural communities. This paper is an attempt toward understanding that everyone who is affected will be impacted differently by climate change both within the same gender and between different genders, including gender minorities. In addition, an attempt is made to identify the drivers of gender-disaggregated social vulnerability in selected disaster-prone rural communities of the district of Dadu, Sindh Province, Pakistan. Both quantitative and qualitative techniques are employed to examine the differences in gender perception on climate change, experiences related to climate change, disasters, and impacts on their lives. Women and households headed by them are found to be relatively more vulnerable due to their socioeconomic and social status in the rural areas of Pakistan. The paper gives policy directives that not only address the measures for reduction in climate change impacts but also suggest the development of effective disaster management programs, policies, and strategies. [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]

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]

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth's energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described. [ABSTRACT FROM AUTHOR]

The unique characteristics of land–air coupling and troposphere–stratosphere interaction over the Tibetan Plateau (TP), the highest landform in the world, play a vital role in weather and climate on regional and global scales. Although a great deal of research has been carried out, large gaps remain in our understanding of TP land–air coupling and its climate effects, due to a lack of observations and the issue of model biases. To address these obstacles, a 10-yr national research program entitled "Changes in the Land–Air Coupled System over the Tibetan Plateau and its Impacts on Global Climate (LASTPIC)" was launched by the National Natural Science Foundation of China in January 2014. What LASTPIC does revolves around three aspects: TP land–air coupled processes; TP's influence on global climate; and reanalysis and model. This paper mainly focuses on the data collection, scientific understanding, and model development of LASTPIC in terms of TP land–atmosphere–ocean coupling and its global climate impacts since program's inception. [ABSTRACT FROM AUTHOR]

Sustainability of rain-fed agriculture in semiarid regions is being threatened by climate variability and change. Weather and climate information (WCI) can be used to reduce the effects of this threat on agricultural production. WCI may be available, but is it readily accessible and communicated/disseminated efficiently to intended end users? Are stakeholders able to interpret the information to correctly inform decision-making? To answer these questions, in view of intermediate stakeholders (service providers to farmers), a study was carried out in two districts of the Midlands Province of Zimbabwe to identify the type and sources of WCI received by these stakeholders as well as constraints and opportunities to access, interpretation, and use of WCI. The study sample was drawn from the Midlands Provincial Drought Relief Committee, a link between the sources of WCI and the smallholder farmers. A questionnaire pretested for clarity and checked for internal consistency of themes using the standardized Cronbach's alpha was used to collect data. Descriptive statistics were generated using SPSS (version 20.0). Findings were that WCI was sometimes not readily available or was received late. Approximately 36% of the intermediate stakeholders (service providers) passed on WCI to farmers in its original form, from the main source the Meteorological Services Department. It was also unfortunate to discover that 36% of the respondents had challenges interpreting WCI. Impediments to the use of WCI by farmers included scientific information not aligning with indigenous information, which is better understood by farmers, and lack of trust in WCI. It is imperative to improve access to WCI and to train stakeholders on interpretation and dissemination of WCI. Significance Statement: Weather and climate information—if accurate, accessible, provided in a timely manner, and well understood—can be helpful in the farm decision-making process. Its usefulness is becoming more important with increased climatic variability and change. This paper highlights the challenges and opportunities for service providers in communicating and disseminating WCI. We believe that findings from the study will give direction to efficient utilization of the information by the various end users, for which smallholder farmers are the majority in most developing countries. [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]

Climate perception is a growing area of study in the social sciences and one that has implications on the tools and strategies we use to communicate climate change risk information. However, the range of climate perception studies remains limited, focused primarily on perceptions of day-to-day weather, sudden-onset severe events, or long-term permanent change. Phenomena situated between these extremes (e.g., annual- to decadal-scale variability) are largely missing from social science of climate research. Whether this is due to limited perception by research participants, is due to limited research attention, or is a reflection of the methods commonly applied to human dimensions of climate research, this gap precludes analysis of the full range of complex climate experiences and their influence on climate perception and understanding. In this paper, we offer a proof of concept for the climate autobiography timeline (CAT), a visual timeline tool developed to assess climate perception while prompting an ordered consideration of time, with the goal of eliciting insights into complex and long-term climate experiences such as low-frequency climate variability. Results are based off a preliminary application of the CAT across focus groups conducted in Newfoundland and Labrador, a province of Canada that is subject to low-frequency climate variability and frequent high-impact weather. Results reveal three key findings: 1) weather and climate narratives are commonly anchored to two time periods, potentially obscuring perceptions of variability; 2) narratives focus on socially important weather and climate phenomena; and 3) the social and visual coconstruction of weather and climate narratives may yield more holistic representations of local climate knowledge. [ABSTRACT FROM AUTHOR]

The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand on the basis of historical weather over the last two decades, thereby ignoring the effects of climate change and the possibility of weather variability outside the recent historical range. In this paper, we develop an empirical method to predict the impact of weather on energy demand. We use that with a large ensemble of climate model runs to construct a probability distribution of power demand on the ERCOT grid for summer and winter 2021. We find that the most severe weather events will use 100% of available power—if anything goes wrong, as it did during the 2021 winter, there will not be sufficient available power. More quantitatively, we estimate a 5% chance that maximum power demand would be within 4.3 and 7.9 GW of ERCOT's estimate of best-case available resources during summer and winter 2021, respectively, and a 20% chance it would be within 7.1 and 17 GW. The shortage of power on the ERCOT grid is partially hidden by the fact that ERCOTs seasonal assessments, which are based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecast an extreme peak load of 67 GW. In reality, we estimate hourly peak demand was 82 GW, 22% above ERCOT's most extreme forecast and about equal to the best-case available power. Given the high stakes, ERCOT should develop probabilistic estimates using modern scientific tools to predict the range of power demand more accurately. [ABSTRACT FROM AUTHOR]

Under the new background of climate change, it is very important to identify the characteristics of drought in North China. Based on the daily meteorological drought comprehensive index from 494 national meteorological stations in North China during 1961–2019, the drought processes and their intensity are identified by applying the "extreme" intensity–duration (EID) theory. Then, the stage variation characteristics of the drought trend, the average drought intensity, and the drought frequency are analyzed. The results show that among the five drought intensity indices the process maximum intensity demonstrates the greatest correlation coefficient with the disaster rate of drought in North China. Therefore, the process maximum intensity of drought is selected as the annual drought intensity to analyze the drought characteristics in North China. According to the climate warming trends, the study period is divided into three stages, that is, 1951–84 (stage I), 1985–97 (stage II), and 1998–2019 (stage III). The comprehensive results show that the drought intensity in North China has significant stage characteristics. In stage I, the drought shows an increasing trend in most parts of North China, but its average intensity is relatively weaker, with a lower severe drought frequency. The drought also shows an increasing trend in most parts in stage II, with a more significant increase rate than that in stage I, and the average drought intensity is the strongest and the severe drought frequency is the highest. In stage III, the drought shows a decreasing trend in some areas, and the average intensity is the weakest, with a lower severe drought frequency. Significance Statement: In this paper, we develop a drought intensity formula, the maximum intensity of drought, based on the "extreme" intensity–duration theory. The maximum intensity of drought was then calculated and selected as an annual drought intensity to analyze the drought characteristics in North China. We found that the annual drought intensity better captured the extremity and the patterns of drought process than that obtained with single indices and comprehensive indices. The results show a decreasing trend of drought in North China after 1998. [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]

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]

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]

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu). [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]

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]

Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [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]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [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]

Large temperature variations in a temperate climate, particularly in late winter and early spring, can be disruptive for native ecosystems and agricultural crops. As warmer temperatures occur earlier in the year in midlatitude regions as a result of anthropogenic climate change, springtime temperatures may become less consistent, leading to potential damage to species and crops that are vulnerable to the return of historically cooler temperatures, including late-spring frosts, after an initial warm-up. In this work, we quantify shifting patterns in late-winter and springtime temperature variations at eight sites across New Jersey from 1950 to 2019. Many sites located along the coast or in the coastal plain experience increases in the number of times the temperature climbs above 15.5°C (60°F) and then falls below freezing (i.e.,0°C, or 32°F). Sites in southern New Jersey (where much of the state's agriculture is located) experience the most significant (P < 0.05) increases in large springtime temperature variations. Across all sites, there is a general increase in both the percentage and magnitude of temperature variations that occur as early as February. At 75% of sites, day-to-day variation in daily maximum temperature has increased from the 1950s through 2019; day-to-day variation in daily minimum temperatures has increased over the same time at more than half of sites considered. These amplifications in extreme temperature variations indicate the need for both mitigation and adaptation strategies to protect vulnerable crops and ecosystems in the region during this critical time of the year. Significance Statement: Human-caused climate change has made it more likely for warmer temperatures to occur earlier in the year, causing many locations to experience late-winter and early-springtime temperatures that are less consistent than they may have been in the past. These variations can be highly problematic for both vital agricultural crops and critical ecosystems. Here, we evaluate how late-winter and early-springtime temperatures have changed throughout New Jersey (home to a variety of agriculture and unique ecosystems) from the mid-twentieth century until 2019. We find critical changes to temperature patterns during late winter and early spring, including larger and more frequent temperature swings (particularly in February) and increased day-to-day variation in high and low temperatures. [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]

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]

This paper addresses the effect of interannual variability in jet stream orientation on weather systems over the North Atlantic basin (NAB). The observational analysis relies on 65 yr of NCEP-NCAR reanalysis (1948-2012). The total daily kinetic energy of the geostrophic wind (GTKE) is taken as a measure of storm activity over the North Atlantic. The NAB is partitioned into four rectangular regions, and the winter average of GTKE is calculated for each quadrant. The spatial GTKE average over all four quadrants shows striking year-to-year variability and is strongly correlated with the North Atlantic Oscillation (NAO). The GTKE strength in the northeast quadrant is closely related to the diffluence angle of the jet stream in the northwest quadrant. To gain insight into the relationship between the diffluence angle and its downstream impact, a quasigeostrophic baroclinic model is used. The results show that an initially zonal jet persists at its initial latitude over 30 days or longer, while a tilted jet propagates meridionally according to the Rossby wave group velocity, unless kept stationary by external forcing. A Gulf Stream-like narrow sea surface temperature (SST) front provides the requisite forcing for an analytical steady-state solution to this problem. This SST front influences the atmospheric jet in the northwest quadrant: it both strengthens the jet and tilts it northward at higher levels, while its effect is opposite at lower levels. Reanalysis data confirm these effects, which are consistent with thermal wind balance. The results suggest that the interannual variability found in the GTKE may be caused by intrinsic variability of the thermal Gulf Stream front. [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]

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]

This paper studies operators inspired by the fluctuation-dissipation theorem that consider the seasonality (nonstationarity) of the climate system under conditions of limited sample size relevant to application of the method to observational records. The approach is used to predict the steady-state response of an atmospheric general circulation model to localized temperature perturbations. A seasonal operator nominally requires a much larger data sample than a stationary operator; the authors study some strategies to overcome this. First, two methods for approximating the seasonality of the system are examined. Second, an alternative 'transpose approach' to the standard dimension reduction is considered that is more efficient and accurate for small sample sizes and additionally enables the use of a kernel, which provides a convenient way to incorporate prior physical understanding into the operator. All operators show considerable skill in predicting seasonal responses for a variety of variables (temperature, winds, rainfall, and cloud cover) and better skill in predicting the annual-mean ones. A comparison of these predictions to ones done on the same system with temporally fixed boundary conditions shows unexpectedly that skill is, if anything, improved by the presence of a seasonal cycle. The authors suggest that the extra complexity due to a seasonal system is outweighed by the added information due to the seasonal forcing and the effect of seasonality in smoothing out prediction errors. [ABSTRACT FROM AUTHOR]

Measurements of air temperature and precipitation at 35 stations in Hubei Province, China, during 1962-2011 are used to investigate the regional climate change. There is an increasing trend for observed air temperature (0.23°C decade−1), which is slightly higher than that from multiple model simulations/predictions [phase 5 of CMIP (CMIP5) datasets] (0.16°C decade−1). The observed precipitation increases at the rate of 11.4 mm decade−1, while the CMIP5 results indicate a much lower decreasing trend (0.8 mm decade−1) in this region. To examine the ecological responses to the climate changes in Hubei Province, annual gross primary productivity (GPP) and net primary productivity (NPP) products during 2000-10 and leaf area index (LAI) products during 1981-2011 are also analyzed. It is discovered that GPP, NPP, and LAI increase at the rate of 1.8 TgC yr−1 yr−1, 1.1 TgC yr−1 yr−1, and 0.14 m2 m−2 decade−1, respectively. A linear model is further used to conduct the correlation analyses between climatic parameters (i.e., air temperature and precipitation) and ecological indicators (i.e., GPP, NPP, and LAI). The results indicate that the air temperature has a significant positive correlation with LAI ( R2 = 0.311) and GPP ( R2 = 0.189); precipitation is positively correlated with NPP ( R2 = 0.209). Thus, it is concluded that the air temperature exerts a stronger effect on the ecosystem than precipitation in Hubei Province over the past decades. [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]

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020-50 would increase under all the scenarios (relative to the baseline period 1970-2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020-50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020-50. The reservoir would be likely to undergo more flooding in 2020-50. [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]

Because they are rare, extreme weather events have critical impacts on societies and ecosystems and attract public and scientific attention. The most unusual events are regularly documented as part of routine climate monitoring by meteorological services. A growing number of attribution studies also aim at quantifying how their probability has evolved under human-induced climate change. However, it is often recognized that (i) the selection of studied events is geographically uneven, and (ii) the definition of a given event, in particular, its spatiotemporal scale, is subjective, which may impact attribution statements. Here we present an original method that objectively selects, defines, and compares extreme events that have occurred worldwide in the recent years. Building on previous work, the event definition consists of automatically selecting the spatiotemporal scale that maximizes the event rarity, accounting for the nonstationary context of climate change. We then explore all years, seasons, and regions and search for the most extreme events. We demonstrate how our searching procedure can be both useful for climate monitoring over a given territory, and resolve the geographical selection bias of attribution studies. Ultimately, we provide a selection of the most exceptional hot and cold events in the recent past, among which are iconic heatwaves such as those seen in 2021 in Canada and in 2003 in Europe. [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]

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]

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China. [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]