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Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (>60°N) significant upward SAT trend of +0.62°C decade−1 since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm. Significance Statement: As a result of global warming, the Arctic Ocean's sea ice is receding, exposing more and more areas to air–sea interactions. This reduces the range of seasonal changes in Arctic surface air temperatures (SAT). Since 1979, the reduced seasonal SAT signal has decreased the trend of warm SAT extremes by 25% over the background warming trend and doubled the trend of cold SAT extremes relative to SAT trends. A substantial number of warm and cold spells would not have been identified as exceptional if the reduction of the Arctic SAT seasonal amplitudes had not been taken into account. As the Arctic continues to warm and sea ice continues to diminish, seasonal SAT fluctuations will become more dampened, with the rate of decreasing winter SAT extremes exceeding the rate of increasing summer SAT extremes. [ABSTRACT FROM AUTHOR]

A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth's surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather. [ABSTRACT FROM AUTHOR]

A main source of regional climate change uncertainty is the large disparity across models in simulating the atmospheric circulation response to global warming. Using the latest suite of global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), a storyline approach is adopted to derive physically plausible scenarios of Antarctic climate change for 2070–99, according to Shared Socioeconomic Pathway SSP5-8.5. These storylines correspond to differences in the simulated amount of seasonal sea ice loss and either (i) the delay in the summertime stratospheric polar vortex (SPV) breakdown or (ii) wintertime SPV strengthening, which together constitute robust drivers of the response pattern to future climate change. Such changes combined are known to exert a strong control over the Southern Hemisphere midlatitude jet stream, which we quantify as collectively explaining up to 70% of the variance in jet response in summer and 35% in winter. For summer, the expected strengthening and displacement of the tropospheric jet stream varies between a ∼1 and 2 m s−1 increase and ∼2°–4° poleward shift, respectively, across storylines. In both seasons, a larger strengthening of the jet is correlated with less Antarctic warming. By contrast, the response in precipitation is more consistent but still strongly attenuated by large-scale dynamics. We find that an increase in high-latitude precipitation around Antarctica is more pronounced for storylines characterized by a greater poleward jet shift, particularly in summer. Our results highlight the usefulness of the storyline approach in illustrating model uncertainty and understanding the processes that determine the spread in projected Antarctic regional climate response. Significance Statement: Uncertainty in future climate predictions for the Antarctic is dominated by the unknown response of the large-scale (global) atmospheric circulation. In characterizing such uncertainty, plausible outcomes of climate response (storylines) are generated from the organization of model projections according to the amount of simulated seasonal sea ice loss and the delay in summertime breakdown/winter strengthening of the stratospheric westerly circulation (polar vortex). The intensity and location of the tropospheric jet stream is strongly dependent on both factors, which strongly influences the near-surface climate response over Antarctica. We find that the simulated amount that Antarctic air temperatures increase by in the future (to the end of the century) is intrinsically related to the projected intensification of the Southern Hemisphere tropospheric jet, varying by a factor of 2 or more across storylines for summer. Storylines with greater jet strengthening are associated with less Antarctic warming (reduced poleward advection of air masses from lower latitudes). Similar differences are found for changes in jet position, which we note has a much stronger control on mid- to high-latitude precipitation response. This includes both an enhanced wetting response around Antarctica and drying response farther equatorward, for storylines characterized by a greater poleward jet shift. [ABSTRACT FROM AUTHOR]

This study identifies a significantly positive relationship between summer surface air temperature (SAT) anomalies over two remote regions in the Eurasian continent and North America during the period 1979–2021 on the interannual time scale. The former region includes the East European Plain and the West Siberian Plain, and the latter region includes central and eastern North America. The regionally averaged summer SAT anomalies show a correlation coefficient of 0.66 between these two regions, which is significant at the 99% confidence level. This intercontinental SAT relationship can be explained by a wavelike pattern of circulation anomalies, which is the leading mode of upper-tropospheric circulation anomalies over the middle and high latitudes of the Northern Hemisphere in summer. Further analysis suggests that the sea surface temperature (SST) anomalies over the Pacific and North Atlantic in the preceding spring, being coupled with the leading mode of atmospheric circulation anomalies over the Pacific–Atlantic sector, persist into summer and affect the SATs in the two remote regions, resulting in the intercontinental SAT connection. Significance Statement: Summer surface air temperature (SAT) has profound effects on public health and agricultural production. Here we find a significantly positive relationship between interannual variations of summer SATs over two remote regions, one in the Eurasian continent and the other in North America. This intercontinental relationship in SATs can be explained as a result of atmosphere–ocean coupling over the Pacific and North Atlantic in the preceding spring. The result is likely to be a critical implication for the seasonal forecast of SAT variations over the two regions. In addition, the concurrence of higher or lower temperatures in these two regions may have impacts on global grain production, since these two regions include many major grain-producing areas in the world. [ABSTRACT FROM AUTHOR]

The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement: The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient. [ABSTRACT FROM AUTHOR]

Although operational weather forecasting centers are increasingly using global coupled atmosphere–ocean–ice models to replace atmosphere-only models for short- and medium-range (10 day) weather forecasting, the influence of sea ice on such forecasting has yet to be fully quantified, especially in the Southern Ocean. To address this gap, a polar-specific version of the Weather Research and Forecasting Model is implemented with a circumpolar Antarctic domain to investigate the impact of daily updates of sea ice concentration on short- and medium- range weather forecasting. A statistically significant improvement in near-surface atmospheric temperature and humidity is shown from +24 to +192 h when updating the daily sea ice concentration in the model. The forecast skill improvements for 2-m temperature and dewpoint temperature are enhanced from June to September, which is the period of late sea ice advance. Regionally, model improvement is shown to occur in most sea ice regions, although the improvement is strongest in the Ross Sea and Weddell Sea sectors. The surface heat budget also shows remarkable improvement in outgoing radiative heat fluxes and both sensible and latent heat fluxes. This idealized research demonstrates the nonnegligible effect of including more accurate time-varying sea ice concentration in numerical weather forecasting. Significance Statement: The purpose of this study is to understand how a more realistic Antarctic sea ice field may influence the skill of short- and medium-range weather forecasts. Many operational atmospheric numerical weather prediction (NWP) models use a static forecast field through the time frame of the model's forecast—often 3–10 days. In this study, we updated the sea ice concentration field daily and compared the forecast outcomes with those from model runs using a static sea ice concentration field. We found the forecast skill of near-surface temperature and humidity show the most significant improvements in our idealized experiments. This indicates the importance of incorporating improved dynamic sea ice representation in Antarctic short- to medium-range operational weather forecasting. [ABSTRACT FROM AUTHOR]

Wet-bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (tens to hundreds of meters). Relative to observations with an International Organization for Standardization (ISO)-compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019–21 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear-sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. from a weather station were on average 0.2°C warmer than Observed WBGT, while the Kestrel 5400 WBGT was 0.7°C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9°C (0.3°–1.4°C) between a tennis court and a neighboring grass field. The method for estimating clear-sky radiation in Ryan and Stolzenbach was most accurate and the clear-sky radiation modified by percentage cloud cover was found to be within 75 W m−2of observations on average. Significance Statement: Wet-bulb globe temperature (WBGT) is a heat stress index that accounts for the effects of air temperature, humidity, wind, and radiation on humans. However, WBGT is not routinely measured at weather stations. This work demonstrated the accuracy of estimating WBGT with methods from Liljegren et al. (2008), finding it to be more accurate than measurements from a popular handheld meter, the Kestrel 5400 Heat Stress Tracker. Variations in WBGT that result in different danger levels were found between measurements over a tennis court and a neighboring grass field, and between sun and shade conditions. Understanding the magnitude of these differences and the biases with WBGT estimates and measurements can inform the planning of outdoor activity to robustly safeguard health. [ABSTRACT FROM AUTHOR]

Using the observational and reanalysis datasets during the period of 1979–2018, this study examines the modulation effects of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on the interdecadal change in summer surface air temperature (SAT) over the eastern Tibetan Plateau (TP). The results show that the eastern TP has experienced a remarkable warming in boreal summer since 1997. In addition, the warming is associated with simultaneously enhanced westward water vapor transport and the northward shift of the subtropical westerly jet (SWJ) over the northeast of the TP. Further research indicates that both water vapor transport and the SWJ are modulated by the PDO and AMO on interdecadal time scales. Since the late 1990s, the PDO has changed from the positive to negative phase, while the AMO has changed from the negative to positive phase. The change in the out-of-phase combination of the PDO and AMO has resulted in a northward shift of the SWJ at 200 hPa near the northeast of the TP through the Rossby wave train originating from the northwestern Atlantic. In addition, the PDO and AMO could both affect the Rossby wave train. From the northwestern Atlantic to Eurasia, the wave train may be dominated by the PDO, while the wave train near the TP is dominated by the AMO. The anomalous anticyclone over the northeastern TP is part of the Rossby wave train, causing the northward shift of the SWJ, favoring the intrusion of the water vapor from the eastern China into the eastern TP, thus leading to the TP warming. Significance Statement: It is understood that the Tibetan Plateau (TP) may be among the most sensitive regions to the global climate change. In addition, this region has warmed very rapidly in the latter half of the twentieth century. Based on the statistical analysis and dynamic diagnostics, the influences of interdecadal variations of atmospheric circulations on summer surface air temperature (SAT) over the eastern TP were analyzed for the years 1979 to 2018. We found that the eastern TP experienced an increase in summer SAT. In addition, the interdecadal change in SAT over the eastern TP is strongly affected by the water vapor transport and the subtropical westerly jet. Further analyses show that the PDO and AMO were the key factors influencing summer SAT over the eastern TP on the interdecadal time scales. The PDO and AMO could both affect the Rossby wave train. From the northwest Atlantic to Eurasia, the wave train may be dominated by the PDO, while the wave train near the TP is dominated by the AMO. In addition, the northwest Atlantic SSTs, which are dominated by the AMO, could also affect the SAT over the eastern TP by the Rossby wave train. [ABSTRACT FROM AUTHOR]

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS's ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model's ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales. [ABSTRACT FROM AUTHOR]

El Niño is known to affect Arctic temperature. However, the robustness of the observed relationship between El Niño and Arctic temperature remains debated. Here we reexamine the impacts of El Niño on the Arctic temperature in boreal winter [December–February (DJF)] using reanalysis datasets and atmospheric model experiments. This study shows that extreme El Niño events are accompanied by significant boreal winter cooling over northeastern Canada and Greenland (NECG), while moderate eastern Pacific (MEP) El Niño events are accompanied by significant boreal winter warming in this region. For central Pacific (CP) El Niño events, a cold signal appears in NECG, but with no statistical significance. During extreme El Niño winters, a positive Pacific–North America (PNA)-like pattern is seen in the Pacific, and anomalously negative 200-hPa geopotential height (Z200) strengthening occurs over NECG, which is a response to anomalous strong wave activity originating in the tropical Pacific. El Niño–induced circulation anomalies can further induce NECG cooling via cold temperature advection and decreased downward longwave radiation. In contrast, for the MEP El Niño, the subtropical jet extends zonally from the North Pacific to the North Atlantic, which is accompanied by increased baroclinicity anomalies and favors the propagation of synoptic eddies into the Atlantic, leading to a negative North Atlantic Oscillation (NAO)-like pattern. This in turn could further enhance the positive Z200 anomalies over NECG, resulting in anomalous warming in NECG through warm temperature advection and enhanced downward longwave radiation. A series of atmospheric model experiments simulates the observed circulation changes and associated warming over NECG. Significance Statement: This work investigates the different impacts of the three El Niño types on regional Arctic wintertime temperature anomalies based on observations and model experiments. The impacts of El Niño events on northeastern Canada and Greenland temperatures during boreal winter show distinct differences between extreme El Niño and moderate EP El Niño events. These distinct differences can be attributed to the different atmospheric circulation patterns induced by different SST patterns, which can lead to warm (cold) temperature advection and enhanced (decreased) downward longwave radiation. These results highlight the different impacts of extreme and moderate EP El Niños on Arctic temperatures and provide an improved understanding of the impact of El Niños on the Arctic climate. [ABSTRACT FROM AUTHOR]

It has been well known that the preceding winter ENSO affects the atmospheric convection over the tropical western North Pacific (WNP) in summer, which has important impacts on Asian climate. However, more than half of the interannual variance in tropical WNP convection cannot be explained by ENSO. This study separates the WNP convection into two components, namely, independent of and dependent on the preceding winter ENSO, and compares the anomalies associated with these two components. The linear regression results indicate that the independent convection suppression corresponds to significant cyclonic anomalies over East Asia in both the lower and upper troposphere, and correspondingly a southward displacement of upper-tropospheric East Asian westerly jet. By contrast, these circulation anomalies are weakened for the dependent convection suppression, which is more closely related to the lower-tropospheric cyclonic anomalies over the Indian Ocean. Accordingly, the independent and dependent components exert distinct impacts on rainfall and temperature in Asia. Specifically, the independent suppression corresponds to more significantly enhanced rainfall in subtropical East Asia compared with the dependent one. Moreover, there are colder surface air temperatures in the midlatitude East Asia for the independent suppression and warmer temperatures in South and Southeast Asia for the dependent suppression. Further analyses suggest that the circulation and climate anomalies for the independent component are mainly contributed by July and August, while those for the dependent component become weak from June to August. These results can be helpful for a better understanding of summer Asian climate variability and predictability. [ABSTRACT FROM AUTHOR]

The South Asian high (SAH) location and intensity are linked with the latent heating of the Tibetan Plateau (TP) and Yangtze River basin. The relationship between SAH variability and its impact on South Asian monsoon (SAM) onset is rarely linked with TP soil moisture. This study uses remotely sensed soil moisture and reanalysis products to quantify the relationship between the TP spring (April–June) soil moisture with SAH and SAM onset during 1988–2008. The results show that the TP spring soil moisture and monsoon onset indices are negatively correlated (R < −0.65), whereas the SAH exhibits a significant positive correlation (R ≥ 0.70) with TP soil moisture. The monsoon onset shows a difference of 20–25 days between the early- and late-onset composites. Significant positive (negative) soil moisture anomalies persist over the TP during the early (late) onset followed by positive (negative) LH (SH) anomalies during early (late)-onset composites. The TP thermal forcing exhibited positive anomalies during the early (late)-onset composites implying significant soil moisture control over the diabatic heating, which favors vertical ascent over the eastern plateau. Such a pattern leads to an earlier formation and movement of the SAH toward the Bay of Bengal (BOB) and southwestward of the TP. Before the early and late monsoon onset composites, the SAH pentad evolution drives the lower-tropospheric westerlies/easterlies toward continental SA. In the Indian Ocean the wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent affecting the onset timing over the Arabian Sea and continental SA with less influence over the BOB monsoon onset. Significance Statement: The Tibetan Plateau heating is one of the key drivers of the Asian monsoon precipitation in the surrounding regions, which has been previously studied in detail. This study explored the Tibetan Plateau spring soil moisture's effect on South Asian monsoon onset timing. The monsoon onset timing is calculated using changes in wind direction, atmospheric temperature, and relative precipitation magnitude. Results found that the spring soil moisture substantially affects the TP thermal heating and the SA monsoon onset timing and highlights the physical processes leading to changes in the monsoon onset timing. The inclusion of soil moisture in estimating the monsoon onset timing can provide a tangible way of improving our understanding of the monsoon and associated water resources management practices. [ABSTRACT FROM AUTHOR]

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean. SIGNIFICANCE STATEMENT: Numerical tools used to simulate the effect of large, offshore wind farms on neighboring farms and the atmosphere are very expensive. Therefore, they can only be computed for a limited number of cases. As temperature is one of the most important parameters in these kinds of simulations, this work provides a new vertical temperature model and an analysis framework that allows for a smart selection of these cases such that they ideally represent the full variation of the atmosphere's temperature profiles. [ABSTRACT FROM AUTHOR]

Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain's synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25%of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1%and 93.5%by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability. SIGNIFICANCE STATEMENT: By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography. [ABSTRACT FROM AUTHOR]

Human heat stress depends jointly on atmospheric temperature and humidity. Wetter soils reduce temperature but also raise humidity, making the collective impact on heat stress unclear. To better understand these interactions, we use ERA5 to examine the coupling between daily average soil moisture and wet-bulb temperature (Tw) and its seasonal and diurnal cycle at global scale. We identify a global soil moisture–Tw coupling pattern with both widespread negative and positive correlations in contrast to the well-established cooling effect of wet soil on dry-bulb temperature. Regions showing positive correlations closely resemble previously identified land–atmosphere coupling hotspots where soil moisture effectively controls surface energy partition. Soil moisture–Tw coupling varies seasonally closely tied to monsoon development, and the positive coupling is slightly stronger and more widespread during nighttime. Local-scale analysis demonstrates a nonlinear structure of soil moisture–Tw coupling with stronger coupling under relatively dry soils. Hot days with high Tw values show wetter-than-normal soil, anomalous high latent and low sensible heat flux from a cooler surface, and a shallower boundary layer. This supports the hypothesis that wetter soil increases Tw by concentrating surface moist enthalpy flux within a shallower boundary layer and reducing free-troposphere-air entrainment. We identify areas of particular interest for future studies on the physical mechanisms of soil moisture–heat stress coupling. Our findings suggest that increasing soil moisture might amplify heat stress over large portions of the world including several densely populated areas. These results also raise questions about the effectiveness of evaporative cooling strategies in ameliorating urban heat stress. Significance Statement: The purpose of this study is to provide a global picture of the relationship between soil moisture anomalies and a heat stress metric that includes the joint effects of temperature and humidity. This is important because a better understanding of this relationship will help improve the prediction of extreme heat stress events and inform strategies for ameliorating heat stress. We find a widespread positive correlation between soil moisture and heat stress, in contrast to studies relying on temperature alone. This raises the possibility that, over much of the world, and in the most populous regions, strategies like irrigation or "greening" that can reduce temperature might be ineffective or even harmful in reducing heat stress with humidity incorporated. [ABSTRACT FROM AUTHOR]

A strong coherence of the summer (June–August) surface air temperature (SAT) anomalies exists over eastern Eurasia (EE) and central Eurasia (CE) in association with a remarkable intraseasonal (10–90-day) variability. The intraseasonal SAT over Eurasia shows a negative correlation between EE and CE, which is closely related to an intraseasonal wave train at the upper troposphere over the mid–high latitudes of Eurasia. The wave train propagates eastward and results in the intraseasonal variation of SAT in a seesaw pattern. The column-integrated temperature budget suggests that both the horizontal and vertical advection are important for the intraseasonal SAT dipole. More specifically, the climatological westerly, intraseasonal meridional wind anomaly, and the anomalous vertical motion play dominant roles in perturbing temperature over the CE and EE regions, while the diabatic heating is a negative feedback. The propagation of the Rossby wave train in the upper troposphere helps to maintain the circulation anomalies associated with the SAT seesaw. The leading modes of meridional wind anomalies at 200 hPa over the Eurasian high latitudes resemble the intraseasonal wave train, indicating that the wave train associated with the SAT seesaw is an atmospheric intrinsic mode over the mid–high latitudes. This study also investigates the impact of the intraseasonal SAT seesaw on regional heat wave events. It is suggested that the intraseasonal SAT seesaw has a major influence on the variability of heat wave events over both EE and CE, which may provide great implications for regional subseasonal forecasts of extreme weather events. [ABSTRACT FROM AUTHOR]

Cold fog forms via various thermodynamic, dynamic, and microphysical processes when the air temperature is less than 0°C. It occurs frequently during the cold season in the western United States yet is challenging to detect using standard observations and is very difficult to predict. The Cold Fog Amongst Complex Terrain (CFACT) project was conceived to investigate the life cycle of cold fog in mountain valleys. The overarching goals of the CFACT project are to 1) investigate the life cycle of cold-fog events over complex terrain with the latest observation technology, 2) improve microphysical parameterizations and visibility algorithms used in numerical weather prediction (NWP) models, and 3) develop data assimilation and analysis methods for current and next-generation (e.g., subkilometer scale) NWP models. The CFACT field campaign took place in Heber Valley, Utah, during January and February 2022, with support from NSF's Lower Atmospheric Observing Facilities (managed by NCAR's Earth Observing Laboratory), the University of Utah, and Ontario Technical University. A network of ground-based and aerial in situ instruments and remote sensing platforms were used to obtain comprehensive measurements of thermodynamic profiles, cloud microphysics, aerosol properties, and environmental dynamics. Nine intensive observation periods (IOPs) explored various mountainous weather and cold-fog conditions. Field observations, NWP forecasts, and large-eddy simulations provided unprecedented data sources to help understand the mechanisms associated with cold-fog weather and to identify and mitigate numerical model deficiencies in simulating winter weather over mountainous terrain. This article summarizes the CFACT field campaign, its observations, and challenges during the field campaign, including real-time fog prediction issues and future analysis. [ABSTRACT FROM AUTHOR]

Large values of convective available potential energy (CAPE) are an important ingredient for many severe convective storms, yet there has been comparatively little research on how, physically, such large values arise or why they take on the observed values and climatology. Here we build on recently published observational and theoretical work to construct a simple, one-dimensional coupled soil–atmosphere model of preconvective boundary layer growth, driven by a single diurnal cycle of prescribed net surface radiation. Based on this model and previously published research, we suggest that high CAPE (>∼1000 J kg−1) results when air masses that have been significantly modified by passage over dry, lightly vegetated soils are advected over moist and/or moderately vegetated soils and then exposed to surface solar heating. Several diurnal cycles may be needed to raise the moist static energy of the boundary layer to levels consistent with high CAPE. The production of CAPE and erosion of convective inhibition (CIN) are strongly affected by the potential temperature of the desert-modified air mass, the level of near-surface soil moisture (and root-zone soil moisture if significant vegetation is present), the type of soil, and the characteristics of the vegetation. Consequently, CAPE production and severe convective weather may be significantly affected by regional-scale land-use changes and by climate change. Significance Statement: The energy available for severe convective storms depends strongly on the properties of the underlying soil and vegetation and the temperature of air masses formed over dry terrain upstream. This implies that the severity of convective storms can be strongly affected by changes in land use and by climate change. [ABSTRACT FROM AUTHOR]

We have developed additive logistic models for the occurrence of lightning, large hail (≥2 cm), and very large hail (≥5 cm) to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients-based approach. The resulting hail models outperform the significant hail parameter, and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the −10°C isotherm was found to be a more universally skillful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modeled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian plains. In the southern plains, a period of enhanced storm activity occurred in the 1980s and 1990s. [ABSTRACT FROM AUTHOR]

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies. Significance Statement: Urban climate simulations are subject to spatially heterogeneous biases in urban air temperatures. Common validation methods using official weather stations do not suffice for detecting these biases. Using a dense set of personal weather sensors in London, we detect these biases before proposing an innovative way to correct them with machine learning techniques. We argue that any urban climate impact study should use such a technique if possible and that urban climate scientists should continue investigating paths to improve our methods. [ABSTRACT FROM AUTHOR]

This study investigates the influence of the Atlantic multidecadal oscillation (AMO) on the multidecadal variability of winter surface air temperature in arid central Asia (ACASAT). Apart from a long-term warming trend, the observational analysis shows that the winter ACASAT exhibits a significant multidecadal variability, which is characterized by antiphase fluctuations with the AMO. The mechanism for this negative correlation between the AMO and the winter ACASAT is explored from the aspect of wave teleconnection. The AMO provides energy for the Scandinavian teleconnection pattern at middle and low altitudes by regulating the high-altitude wave train over the middle and high latitudes of Eurasia, and thus has an impact on the remote climate in arid central Asia. Results from the linear baroclinic model (LBM) provide evidence for the linkage between the AMO and the Scandinavian teleconnection pattern. When the AMO is in its warm periods, the Scandinavian teleconnection pattern is in a positive phase, which further makes the cold air from the northeast strengthen, leading to the anomalously colder surface air temperature in arid central Asia. Based on the relationship that the North Atlantic Oscillation (NAO) leads the AMO by 15–20 years, it is further found that there is a leading relationship between the NAO and the winter ACASAT via the AMO. On this basis, an empirical model using the NAO as a predictor was established to predict the ACASAT, and the empirical model shows good hindcast performance. Results from the model show that the winter ACASAT will continue to rise in the next 10 years and decline after 2030. [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]

Using NCEP–NCAR reanalysis data, the atmospheric intraseasonal oscillation (ISO) over the mid-high latitudes in the Southern Hemisphere during austral summer is studied. Two types of 10–30-day eastward- and westward-propagating ISO at mid-high latitudes are extracted by extended empirical orthogonal functions. The analysis of wave activity flux reveals that the energy sources of the eastward-propagating wave train are in the southwest Pacific and southern Indian Ocean, and the westward-propagating wave train is over the east coast of South America. The diagnosis of geopotential height tendency shows that the zonal gradient of ISO relative vorticity guided by mean zonal wind plays a major role in the eastward propagation of the wave train, while the meridional gradient of mean relative vorticity and geostrophic vorticity guided by ISO meridional wind plays a major role in the westward propagation of the wave train. Energy analysis shows that the amplitude enhancement over the South Pacific is due to the ISO disturbance gaining energy from the mean flow. The eastward-propagating ISO obtains energy from the mean flow through kinetic and potential energy conversion, while the westward-propagating ISO cannot obtain energy through potential energy conversion. The surface air temperature and precipitation in South America are affected by the circulation and propagation corresponding to both types of ISO. The useful forecasting skills by the subseasonal-to-seasonal forecasting project for eastward- and westward-propagating types can reach to 17 days, while the potential forecasting skills improve to 20 and 19 days, respectively. Significance Statement: The intraseasonal oscillation (ISO) has important effects on regional weather and climate, and we focus on the ISO over mid-high latitudes in the Southern Hemisphere, which has been paid little attention. We reveal two leading types of the mid-high-latitude ISO during austral summer, namely, the eastward- and westward-propagating types. Also, the characteristics and effects of the two types are explored. To understand their dynamical process, the geopotential height tendency and energy conversion are diagnosed. It is also found that the two ISO types can be predicted up to 17 days in advance by the subseasonal-to-seasonal project, which may provide guidance for the extended-range forecasts. [ABSTRACT FROM AUTHOR]

Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951-2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses. [ABSTRACT FROM AUTHOR]

To investigate patterns of horizontal atmospheric latent energy (LE) transport toward the Arctic, we applied the self-organizing maps (SOM) method to the daily vertically integrated horizontal LE flux from ERA5 in winter (January–March) during 1979–2021. A clear picture depicting the LE transport to the Arctic at a synoptic scale then emerged, with four primary transport pathways identified: the northern Europe, the Davis Strait, the Greenland Sea, and the Bering Strait pathways. The four primary pathways occurred at a comparable frequency, and noticeable interannual variability was observed in their time series of frequency during 1979–2021. Further analysis suggested that the northward LE transport through all these pathways is significantly modulated by cyclones, with the northern Europe and the Greenland Sea pathways being mostly affected. Generally, more frequent and stronger cyclones were observed near the entry regions of LE transport compared to other regions. Moreover, this study provides a comprehensive picture of how atmospheric LE transport is related to air temperature, moisture, surface heat flux, and sea ice anomalies over the Arctic Ocean in winter. Through a thermodynamic perspective, we argue that the deleterious impacts of poleward LE transport on Arctic sea ice are to a large extent attributable to the enhanced local atmosphere–ice interactions, which increase downward longwave radiation (DLR) plus turbulent fluxes, consequently warming the surface and promoting the loss of sea ice. According to the quantitative results, among the four primary pathways, LE transport through the Davis Strait and the Greenland Sea could cause the loss of Arctic sea ice most efficiently. [ABSTRACT FROM AUTHOR]

The Arctic is warming faster than anywhere on Earth, and with these warming temperatures, there is likely to be more precipitation falling as rain. This precipitation phase change will have profound impacts on the hydrologic cycle, energy balance, and snow and sea ice mass budgets. Here, we examine the number of rainfall days in the Arctic from three reanalyses, ERA-Interim, ERA5, and MERRA-2, over 1980–2016. We show that the number of rainfall days has increased over this period, predominantly in the autumn and in the North Atlantic and peripheral seas, and the length of the rain season has increased in all reanalyses. This is positively correlated to the number of days with above freezing air temperatures and a lengthening of the warm season. ERA-Interim produces significantly more rainfall days than other reanalyses and CloudSat observations, as well as significantly more rainfall when temperatures are below freezing. Investigation into the cloud microphysics schemes revealed that the scheme employed by ERA-Interim allowed for mixed-phase clouds to form rain at temperatures below freezing following a temperature-dependent phase partitioning function between 250 and 273 K. This simple diagnostic treatment erroneously overestimates rain at temperatures below 273 K and produces unrealistic rainfall compared to ERA5 and MERRA-2. This work highlights the importance of having accurate physics and improving microphysical schemes in models for simulating precipitation in the Arctic and the caution that is warranted for interpreting reanalysis trends. [ABSTRACT FROM AUTHOR]

Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed. [ABSTRACT FROM AUTHOR]

The predictability of temperatures in North America is of great importance for local agriculture and human health. In autumn (SON), the temperature in North America is correlated with ENSO, but its predictive skill is limited. Here, we show that spring (MAM) sea surface temperature (SST) anomalies in the southwest Pacific (SWP) exhibit a higher correlation with autumn temperature in North America (r = 0.73) than ENSO. This cross-seasonal and cross-hemispheric relationship is established via the western tropical Pacific (WTP) region. The spring SWP SST anomalies show a cross-hemispheric propagation embedded in the southerly monsoonal flow through the wind–evaporation–SST feedback, which sustains the progression of SST anomalies toward the WTP in autumn. The AGCM simulations from multimodels show that active convection in the WTP caused by SST warming stimulate a Rossby wave train propagating downward to North America, where it is governed by an anomalous high with increased atmospheric thickness and tropospheric temperature. Consequently, the longwave radiative heating over North America is enhanced, raising the surface air temperature. These results indicate that the SWP SST is a useful predictor of North American temperature 6 months in advance through the cross-hemispheric influence. A model based on the spring SWP SST and preceding winter ENSO (Niño-3.4) shows a high predictive skill for the autumn temperature anomalies in North America. Significance Statement: Seasonal predictions of surface temperature are important for agricultural decision-making, disaster prevention, and mitigation. Over the western Pacific, associated with the seasonal evolution of ITCZ, there is a strong northward progression of SST anomaly signals generated in the southwest Pacific. This study finds that this cross-hemispheric SST propagation can significantly impact the autumn surface temperature over central North America through the atmospheric bridge. A statistical model incorporating southwest Pacific SST for spring is constructed to predict the North American autumn surface temperature and exhibits high consistency with observations. This improves our understanding of the interhemispheric interactions at seasonal time scales and the seasonal predictability of the North American climate. [ABSTRACT FROM AUTHOR]

The premonsoon circulation over South Asia in May shows remarkable interannual variations, and it can modulate the onset of the Asian summer monsoon. The present study derives two dominant stratosphere–troposphere modes over the North Atlantic in April via applying principal component (PC) analysis to regional geopotential height during 1979–2015. Both of the modes reflect the North Atlantic Oscillation (NAO)-like circulation at the midtroposphere, but the circulation patterns and planetary wave activity between the two modes are quite different. The first mode represents a stratosphere–troposphere-coupled mode. Further analysis indicates that during the years with a positive phase of the first mode, the low-level anomalous southerlies over the northern Barents Sea (BS) lead to a reduction in local sea ice, which could persist into May through the ice-albedo feedback. The BS sea ice anomalies in May could generate a southeastward propagating Rossby wave train, producing an anomalous anticyclonic circulation to the west of Qinghai–Tibet Plateau. This anomalous anticyclone induces increases in local air temperature and the meridional temperature gradient, resulting in a strengthened premonsoon circulation. Given that prediction of the premonsoon circulation over South Asia remains a challenging issue, the stratosphere–troposphere-coupling modes in April provide another potential predictability source. On the other hand, ENSO has a significant effect on the South Asian premonsoon circulations in May, and can also lead to the NAO-like circulation by influencing atmospheric Rossby waves. Such an NAO-like circulation is closely related to the second mode. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming, the Eurasian continent and its surrounding areas have experienced ununiform warming in recent decades. Variations in the meridional thermal contrast (MTC) in East Asia and its adjacent regions have a critical influence on the East Asian summer monsoon (EASM); therefore, its forcings were analyzed in this study. The results show that the MTC in East Asia and the surrounding regions showed significant decadal changes during the interval 1960–2014, with a decadal strengthening in the late 1970s and a decadal weakening in the late 1990s. Further investigation showed that internal variability was the dominant control on these two decadal changes of MTC, accounting for 135% and 78.5% of the two observed changes in the 1970s and 1990s, respectively. Based on pacemaker experiments and a method that reconstructs decadal changes in the interdecadal Pacific oscillation (IPO), we found that a positive phase of the IPO tends to cause cooling in the low latitudes and warming in the mid–high latitudes and that a negative phase of the IPO tends to cause warming in the low latitudes and cooling in the mid–high latitudes, which in turn can explain 54.4% and 35.8% of the two internally generated decadal changes in the MTC, respectively. The Atlantic multidecadal oscillation (AMO), when in the same phase as the IPO, exhibits an opposing pattern of surface air temperature. This only had a significant influence in the late 1990s, where it explained 37.9% of the internally generated decadal changes in the MTC. Our findings highlight that internal variability, especially that relating to the IPO and the AMO, should be considered when predicting future changes in thermal conditions in East Asia and its adjacent regions. [ABSTRACT FROM AUTHOR]

This study examines the process of water-column stratification breakdown in Antarctic coastal polynyas adjacent to an ice shelf with a cavity underneath. This first part of a two-part sequence seeks to quantify the influence of offshore katabatic winds, alongshore winds, air temperature, and initial ambient stratification on the time scales of polynya destratification through combining process-oriented numerical simulations and analytical scaling. In particular, the often-neglected influence of wind-driven circulation on the lateral transport of the water formed at the polynya surface—which we call Polynya Source Water (PSW)—is systematically examined here. First, an ice shelf–sea ice–ocean coupled numerical model is adapted to simulate the process of PSW formation in polynyas of various configurations. The simulations highlight that (i) before reaching the bottom, majority of the PSW is actually carried away from the polynya by katabatic wind–induced offshore outflow, diminishing water-column mixing in the polynya and intrusion of the PSW into the neighboring ice shelf cavity, and (ii) alongshore coastal easterly winds, through inducing onshore Ekman transport, reduce offshore loss of the PSW and enhance polynya mixing and PSW intrusion into the cavity. Second, an analytical scaling of the destratification time scale is derived based on fundamental physical principles to quantitatively synthesize the influence of the physical factors, which is then verified by independent numerical sensitivity simulations. This work provides insights into the mechanisms that drive temporal and cross-polynya variations in stratification and PSW formation in Antarctic coastal polynyas, and establishes a framework for studying differences among the polynyas in the ocean. [ABSTRACT FROM AUTHOR]

Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users. [ABSTRACT FROM AUTHOR]

As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal-mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures the decadal variability of annual-mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific decadal oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower-tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability. Significance Statement: The amplitude of decadal variability of seasonal-mean or monthly mean variables exhibits seasonality. Our results show that the seasonality in decadal variability is consistent with the seasonality in interannual variability. We also identified that the seasonality in oceanic decadal variability is weak and smaller than that in atmospheric decadal variability. Decadal variability of lower-tropospheric atmospheric circulation exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. However, the significant seasonality in decadal variability of precipitation occurs in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into the seasonality of decadal variability in climate systems. [ABSTRACT FROM AUTHOR]

We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region. [ABSTRACT FROM AUTHOR]

A new wintertime surface air temperature (SAT) pattern, called the Asia–Kuroshio and Oyashio Extension–North America (AKNA) pattern, is identified over the pan–North Pacific region (85°E–85°W, 25°–65°N) based on NCEP reanalysis data. The AKNA pattern is likely to influence the climate of the extratropical area of Asia and North America via two SAT dipoles and has a significant impact on the wintertime extremely cold weather along the eastern coastal regions of East Asia. Simulations using an atmospheric general circulation model indicate that wintertime sea surface temperature anomalies (SSTa) in the Kuroshio and Oyashio Extension (KOE) region can force an equivalent barotropic atmospheric ridge downstream and weaken the Siberian high and Alaska atmospheric ridge, resulting in the formation of the AKNA pattern. This circulation pattern tends to intensify the midlatitude (40°–60°N) westerlies over East Asia, which inhibits the southward invasion of the cold air into southern East Asia. Further diagnostic analysis indicates that the KOE SSTa can modulate the variation of storm track and westerlies by affecting baroclinic instability and eddy–mean flow interaction. Moreover, the KOE SSTa can provide a favorable environment for the development of the local atmospheric ascending motion and secondary circulation across the KOE SSTa, thereby affecting variability of the free atmosphere. Significance Statement: This study aims to build a connection between the wintertime extratropical climate and the variation of the Kuroshio and Oyashio Extension. This work isolated a new wintertime surface air temperature (SAT) pattern over mid–high-latitude Asia and North America, which explains a considerable proportion of cold extremes over the eastern regions of East Asia. The reanalysis data and model simulations indicate that the temporal variability of the SAT pattern is influenced by the change of sea surface temperature in the Kuroshio and Oyashio Extension. These findings emphasize the important role of midlatitude air–sea interaction in the modulation of the mid–high-latitude climate. [ABSTRACT FROM AUTHOR]

Snow cover (SC) is an important contributor to atmospheric predictability on subseasonal to seasonal time scales. This paper evaluates the submonthly scale cause-and-effect relationship between SC and surface air temperature (SAT) in Eurasia, which has been typically overlooked in previous statistical analyses of subseasonal to seasonal atmospheric predictability. We focus on the November east–west dipolar SC pattern, a dominant large-scale SC phenomenon. We use an information flow analysis, based on information theory, to infer causal relations not revealed by conventional correlation analysis. This analysis indicates a one-way causality from SAT to SC around the west pole (Europe) in boreal autumn (November), implying that SC has little influence on the time evolution of SAT. In contrast, causality from SC to SAT is significant around the east pole (the Mongolian Plateau). An atmospheric model experiment suggests that the SAT response to SC can persist for a month via snow–albedo feedback, although the response of the upper atmosphere in the model is small. Furthermore, the subseasonal hindcasts show that the contribution of SC may affect the predictability of SAT for up to four weeks around the east pole. We suggest that the geographical and climatological atmospheric conditions are favorable for generating a positive albedo feedback as a “hotspot” of SC–SAT coupling around the east pole in autumn. The agreement of our causality analysis with other analytical and modeling approaches underscores the cause-and-effect relationship between SC and SAT and its contribution to the subseasonal predictability over autumnal Eurasia. [ABSTRACT FROM AUTHOR]

Recent analysis of pantropical interactions suggests that after 1980 the tropical Atlantic Ocean’s (TAO) influence on the tropical Pacific Ocean (TPO) appears to have become much more pronounced while the tropical Indian Ocean’s (TIO) influence appears to have weakened. This study explores whether and how decadal changes in TAO and TPO SSTs modulate these pantropical connections in an attempt to explain the recent dominance of the TAO. To this end, we carry out a series of idealized atmosphere-only experiments using the ACCESS atmospheric general circulation model where the magnitude and sign of the decadal TAO SST signal are varied, presenting various warm and cool Atlantic scenarios. To understand further if these pantropical connections are influenced by changes in TPO SST, we carry out the above TAO experiments with both warm and cool phases of Pacific decadal variability (PDV). We find that an imposed TAO warming leads to increases in TPO atmospheric temperature and stability, which lead to a decrease in average TPO precipitation, with the most prominent changes occurring in June–August. These changes in TPO precipitation induced by TAO warming are largely mirrored when TAO cooling is added, whereas the TPO rainfall response to TAO anomalies remains relatively unchanged for the different phases of PDV. In contrast to the precipitation response, the wind response did display some asymmetries between different phases of TAO SST variability. Specifically, surface winds in the western half of the Niño-4 region exhibited a significantly different response to positive versus negative Atlantic multidecadal variability (AMV), whereas the surface winds in the western equatorial Pacific were significantly stronger (roughly 40% larger) in the positive phase of PDV than in the negative phase. These results suggest that the phases of PDV and AMV may modulate pantropical interactions through their effect on zonal wind stress. [ABSTRACT FROM AUTHOR]

A new mixed scaling parameter Z = z/(Lh)1/2 is proposed for similarity in the stable atmospheric surface layer, where z is the height, L is the Obukhov length, and h is the boundary layer depth. In comparison with the parameter ζ = z/L from Monin–Obukhov similarity theory (MOST), the new parameter Z leads to improved mean profile similarity for wind speed and air temperature in large-eddy simulations. It also yields the same linear similarity relation for CASES-99 field measurements, including in the strongly stable (but still turbulent) regime where large deviations from MOST are observed. Results further suggest that similarity for turbulent energy dissipation rate depends on both Z and ζ. The proposed mixed scaling of Z and relevance of h can be explained by physical arguments related to the limit of z-less stratification that is reached asymptotically above the surface layer. The presented evidence and fitted similarity relations are promising, but the results and arguments are limited to a small sample of idealized stationary stable boundary layers. Corroboration is needed from independent datasets and analyses, including for complex and transient conditions not tested here. [ABSTRACT FROM AUTHOR]

To reduce the amount of nonclimatic biases of air temperature in each weather station's record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an oftenoverlooked statistical problem known as "urban blending" or "aliasing of trend biases." This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ~60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ~20%of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC's estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) exerts strong powerful thermal forcing, which plays a vital role in influencing weather–climate variations in Asia and even the Northern Hemisphere. However, the causes of thermal variation over the TP have not been fully revealed. Here, the role of winter soil moisture (SM) in subsequent summer thermal anomalies on the TP was investigated through multisource datasets for the period 1979–2014. Results indicate a significant positive relationship (r = 0.52) between winter SM and subsequent summer-mean surface air temperature (SAT). Further investigations show that more (less) winter SM results in abundant (deficient) atmospheric water vapor in subsequent summer owing to its persistence. Furthermore, Earth’s surface energy budget equation confirms that strengthened (weakened) surface downward longwave radiation caused by increased (decreased) water vapor is the dominant factor leading to SAT variations, even more significant for nocturnal SAT. The winter SM–atmospheric temperature positive relationship can extend from the surface to 200 hPa over the TP. In addition, the enhanced (weakened) atmospheric latent heat release associated with increased (decreased) water vapor content may be another important factor contributing to changes in atmospheric temperatures over the TP. Therefore, our results contribute to a better understanding of the effect of land–surface processes on thermal anomalies over the TP. Significance Statement Understanding the causes of thermal anomalies over the Tibetan Plateau (TP) is crucial for weather and climate variations, but the role of winter soil moisture (SM) in subsequent summer thermal effects is largely unknown. This study investigated the relationship between winter SM and thermal anomalies in subsequent summers on the TP. Results show that the above (below)-normal winter SM will cause warm (cold) atmospheric temperatures in the subsequent summer. The above (below)-normal winter SM anomalies bring increased (decreased) atmospheric water vapor in summer owing to its persistence, resulting in strengthened (weakened) downward longwave radiation and atmospheric latent heat release to heat (cool) the atmosphere. [ABSTRACT FROM AUTHOR]

It is well known that El Niño–Southern Oscillation (ENSO) can influence the East Asian winter climate by modifying the atmospheric circulation over the western North Pacific (WNP). While the impact on precipitation in southeastern China has been extensively studied, the ENSO signal in surface air temperature (SAT) remains overlooked. In this paper, we identify robust ENSO footprints in the winter daily minimum SAT in southeastern China, with El Niño winters generally accompanied by warmer-than-normal minimum SAT anomalies. In contrast, the responses of maximum SAT are weak and negligible, suggesting a diurnal cycle–dependent ENSO influence. Further analysis indicates that this diurnal cycle dependence stems primarily from the disparate surface radiative heating between day and night induced by ENSO-related local total cloud cover (TCC) change. The warmer minimum SAT occurring in the early morning of El Niño winters is mainly caused by the enhanced surface downward longwave radiative heating as a result of the TCC increase. However, in the afternoon of El Niño winters, although the anomalous horizontal advection of warm air plays a role, there is surface radiative cooling as the weakening of solar radiation due to TCC reflection overwhelms the increase in downward longwave radiation, which leads to a weakened sensible heat flux and thus has a cooling effect on the SAT. Ultimately, these two processes effectively cancel each other out and together produce insignificant maximum SAT responses. Our conclusions carry important implications for the seasonal to interannual winter climate prediction in southeastern China. Significance Statement The purpose of this study is to better understand how ENSO influences winter surface air temperature in southeastern China. Despite insignificant responses of winter daily maximum temperature to ENSO, we find here a robust ENSO influence on the daily minimum temperature, with warm anomalies tending to occur during El Niño events. Further evidence suggests that this diurnal cycle–dependent ENSO influence is mainly caused by ENSO-induced change in local cloudiness, which has a different radiative heating effect between day and night. Our findings are of great importance for improving regional climate prediction, which will benefit social and economic activities. [ABSTRACT FROM AUTHOR]

Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection patterns of 2-m surface air temperature (T2m) in North America, where very low temperatures in the contiguous United States may coincide with very high temperatures in Alaska in the wintertime. This dependence between warm and cold extremes motivates the need for a model with opposite-tail dependence in spatial extremes. This work develops a statistical modeling framework that has flexible behavior in all four pairings of high and low extremes at pairs of locations. In particular, we use a mixture of rotations of common Archimedean copulas to capture various combinations of four-corner tail dependence. We study teleconnected T2m extremes using ERA5 of daily average 2-m temperature during the boreal winter. The estimated mixture model quantifies the strength of opposite-tail dependence between warm temperatures in Alaska and cold temperatures in the midlatitudes of North America, as well as the reverse pattern. These dependence patterns are shown to correspond to blocked and zonal patterns of midtropospheric flow. This analysis extends the classical notion of correlation-based teleconnections to considering dependence in higher quantiles. [ABSTRACT FROM AUTHOR]

Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957-2021; n 5 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m. 0.508C decade21). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature-sea ice feedbacks on theNorth Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska's climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show; 50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming. [ABSTRACT FROM AUTHOR]

We investigate the land-ocean warming contrast mechanisms, f, defined as the land-mean surface air temperature (SAT) change divided by the ocean-mean SAT change, in a transient climate response (TCR) obtained from the Coupled Model Intercomparison Project phase 6 (CMIP6) 1% per year CO2 increase experiments (1pctCO2). The energy budget framework devised in Part I is applied to 15 CMIP6 1pctCO2 simulations, and the climate response in year 140 when the CO2 concentration was quadrupled was compared with a near-equilibrium climate response (NEQ), defined as the last 30-yr mean in the abrupt CO2 quadrupling (abrupt43CO2) experiments. It is shown that f is larger in TCR than in NEQ by approximately 4%, although the difference is not statistically significant. In TCR, effective radiative forcing is large over land compared to the ocean, and this is the main contributor to f as in NEQ. The time evolution of f in 1pctCO2 can be reconstructed by means of the fast and slow components of climate response in abrupt43CO2, indicating that the essential mechanism for the land-ocean warming contrast shown in Part I applies to TCR. Furthermore, our analyses reveal a compensation between land-to-ocean atmospheric energy transport that decreases f and ocean heat uptake that increases f. Regardless of the time scale of the response, these two processes are linked by the change in atmospheric circulation, leading to the small combined effect. As a result, the multimodel mean f in 1pctCO2 is roughly time invariant at approximately 1.5 despite the continuous increase in CO2. [ABSTRACT FROM AUTHOR]

The influence of mid–high-latitude intraseasonal variability (ISV) on the occurrence frequency of the Northeast China cold vortex (NCCV) in early summer was examined through statistical analysis and thermal–dynamic diagnostics. A multivariable empirical orthogonal function (MVEOF) was employed to extract the thermal–pressure coupled ISV mode. Our results show that the geopotential height and air temperature over the NCCV active region exhibit a statistically significant intraseasonal periodicity of 20–60 days. The dominant ISV mode features a westward-propagated zonal dipole pattern, which is generated over the Lake Baikal region and triggered by intraseasonal wave energy accumulation. By dividing the ISV cycle into eight phases, it is found that more NCCVs with a large scope occur in phases 5–8 than those in phases 1–4. The positive (negative) geopotential height and air temperature tendencies in phases 1–4 (5–8) act to suppress (facilitate) the NCCV activity. The thermodynamic tendency budget and scale decomposition reveal that when an anomalous intraseasonal cyclonic circulation propagates westward from Lake Baikal to the Ural Mountains, the anomalous southwesterly transports mean negative vorticity from the north side of the Tibetan Plateau to Northeast Asia and transports mean warm air temperature from low latitudes to high latitudes, leading to the positive geopotential height and air temperature tendencies and thereby restraining the NCCV activity. The opposite is also true for the facilitation of the NCCV modulated by the negative geopotential height and air temperature tendencies. Significance Statement: The purpose of this study is to better understand the factors controlling the Northeast China cold vortex (NCCV) activity in early summer. It is important because influences of the subtropical monsoonal circulation are usually confined to southern China in this season and the anomalous atmospheric circulation from the mid–high latitudes plays a more important role in the generation of the NCCV. Our results provide a guide on how intraseasonal variability at mid–high latitudes controls the occurrence frequency of the NCCV and highlight the process of thermal and dynamical modulation in the NCCV. [ABSTRACT FROM AUTHOR]

The features and causes of the leading intermonth modes of winter surface air temperature anomalies (SATA) over China are investigated, and associated prediction models are developed. The first three intermonth modes of winter SATA over China are obtained by extended empirical orthogonal function (i.e., EEOF1–3) analysis. The results show that EEOF1 represents consistent variations in the whole winter, with a variance contribution of 32.3%, whereas EEOF2 and EEOF3 show spatiotemporally inconsistent changes, and their variance contributions are 16.9% and 12.5%, respectively. EEOF2 has out-of-phase variations between December and January–February, and EEOF3 exhibits a temporal warm–cold alternating pattern, with spatially reversing changes over northwestern and southern China. However, the Climate Forecast System, version 2 (CFSv2) presents a limited prediction skill for winter SATA over China and their intermonth modes. Further investigations indicate that the September sea ice over the Barents–Laptev Seas, the November snow cover over western Europe and East Asia, and the November northern Atlantic sea surface temperature can be, respectively, adopted to develop prediction schemes for the consistent mode (EEOF1 scheme) and two inconsistent modes (EEOF2 and EEOF3 schemes) based on specific mechanisms. These schemes show effective performances in predicting both individual modes and the reconstruction field of SATA over China. The temporal correlation coefficients (TCCs) between cross-validation results and observations are 0.48, 0.51, and 0.31 for the EEOF1–3 modes, respectively (the 90% confidence level is 0.27). For the reconstruction field, the TCCs are 0.40, 0.27, and 0.45 in December, January, and February, respectively, which are much higher than those of the CFSv2 outputs (0.23, −0.16, and −0.09). [ABSTRACT FROM AUTHOR]

In North China (NC), heat stress, which can be quantitatively characterized by wet-bulb globe temperature (WBGT), is closely related to specific humidity. This study focuses on the total days for NC average daily maximum WBGT exceeding 26°C (WGBT26) per summer (June–August) from 1979 to 2017. Rather than local precipitation or evaporation in NC, the NC WBGT26 is significantly related to the nonlocal evaporation around the Yangtze River valley (YR). The abnormal positive evaporated water vapor in YR, associated with anomalously high water vapor flux from south to north at 925 hPa, is continuously transported to NC in the above-normal WBGT26 years. Such an abnormal "evaporation and transportation" process can significantly increase the water vapor in NC and therefore enhance WBGT26. The evaporation in YR peaks in mid- to late July and is closely associated with the occurrence days for daily maximum WBGT exceeding 26°C and maximum daily mean specific humidity at 925 hPa in summer. The main driver for the strongest YR evaporation anomaly from 15 July to 15 August is the simultaneous surface air temperature rather than the simultaneous or earlier soil moisture, precipitation, and vapor pressure deficit (VPD) anomaly. This condition is due to the continuously abundant soil moisture in the YR from April to September. The results of this study provide new ideas for studying heat stress in NC, indicating that nonlocal land–atmosphere interactions are crucial. [ABSTRACT FROM AUTHOR]

Longwave radiation (LR) is one of the energy balance components responsible for warming and cooling water during hot summers. Both downward incoming LR, emitted by the atmosphere, and outgoing LR emitted by the land surface are not widely measured. The influence of clouds on the LR heat budget makes it even harder to establish reliable formulations for all-sky conditions. This paper uses air temperature and cloud cover from the ERA5 reanalysis database to compare 20 models for the downward longwave irradiance (DLI) at Earth's surface and compare them with ERA5's DLI product. Our work uses long-time continuous DLI measured data at three stations over Canada, and ERA5 reanalysis, a reliable source for data-scarce regions, such as central British Columbia (Canada). The results show the feasibility of the local calibration of different formulations using ERA5 reanalysis data for all-sky conditions with RMSE metrics ranging from 37.1 to 267.3 W m−2, which is comparable with ERA5 reanalysis data and can easily be applied at broader scales by implementing it into hydrological models. Moreover, it is shown that ERA5 gridded data for DLI shows the best results with RMSE = 31.7 W m−2. This higher performance suggests using ERA5 data directly as input data for hydrological and ecological models. [ABSTRACT FROM AUTHOR]

Subseasonal forecasts have recently attracted widespread interest yet remain a challenging issue. A statistical Kalman filter pattern projection method (KFPPM), which absorbs the projection conception of the raw covariance pattern projection (COVPPM) and the adaptive adjustments of the Kalman filter, is proposed to calibrate the single-model forecasts of the daily maximum and minimum temperatures (Tmax and Tmin) for lead times of 8-42 days over East Asia in 2018 derived from the UKMO control (CTL) forecast. The Kalman filter-based gridly calibration (KFGC) is carried out in parallel as a benchmark, which could improve the forecast skills to a certain extent. The COVPPM effectively calibrates the temperature forecasts at the early stage and displays better performances than the CTL and KFGC. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0° and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal time scale, showing the most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Though the postprocessing procedures are more skillful in calibrating Tmax forecasts than Tmin forecasts, the Tmax forecasts are still characterized by lower skills than the latter. Case experiments further demonstrate the abovementioned features and imply the potential capability of KFPPM in improving forecast skills and disaster preventions for extreme temperature events. [ABSTRACT FROM AUTHOR]

The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land-atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR's performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m³ m-3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR's biases to locations with subgrid-scale land cover variations, general knowledge of HRRR's biases will help guide improvements to land surface models used in high-resolution weather forecasting models. [ABSTRACT FROM AUTHOR]