Land-atmosphere Feedbacks Research Articles

The characterization, mechanism, predictability, and impacts of the unprecedented 2023 Southeast Asia heatwave

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks

Abstract Transpiration (T), the component of evapotranspiration (ET) controlled by the vegetation, dominates terrestrial ET in many ecosystems; however, estimating it accurately, especially at the global scale, remains a considerable challenge. Existing approaches mostly rely on the relationship between T and photosynthesis, but untangling this relationship is difficult and leads to diverging T estimates. Limited in-situ measurements and the inability to directly measure transpiration from space further complicate the reliable assessment of this crucial process in the terrestrial water cycle. Here, we developed a new hybrid Priestley-Taylor (PT) model combined with an Artificial Neural Network (ANN) using globally available remote sensing and reanalysis data of soil moisture, vapor pressure deficit and windspeed. We also take advantage of the newly released global sap flow measurement network SAPFLUXNET. In the proposed approach, we avoid the parameterization of stomatal conductance by training the ANN on the PT-Coefficient α, obtained by inverting the PT equation. The results showed that our model framework can estimate T in different forest ecosystems based on few predictors. By utilizing forcings from independent datasets, we eliminate the reliance on in-situ measurements for predicting T. Through upscaling actual observations to a larger scale, this model framework helps alleviate the scarcity of T products. Intercomparison of T with ET partitioning methods based on eddy covariance data, shows high performances (KGE of 0.69 in Europe and 0.60 in North America), slightly improving estimates compared to other models. Analysis of contribution of T to ET across 100 FLUXNET sites result in a global mean of 55.2%. We believe that modelling T independent from the carbon cycle can support our understanding of land-atmosphere feedbacks and climate extremes in future research.

The flash droughts across the south-central United States in 2022: Drivers, predictability, and impacts

A rare subseasonal-to-seasonal phenomenon – two consecutive flash drought events interrupted by a period of recovery – occurred across eastern Oklahoma, Arkansas, and southern Missouri, spanning the summer and early fall of 2022. These flash drought events (the first in June–July, the second in August–September) led to severe (D2) and extreme (D3) drought conditions via the United States Drought Monitor across much of the region following the first period of rapid drought intensification, and extreme (D3) and exceptional (D4) drought conditions by the end of the second event. A notable driver of both flash drought events included a persistent upper-level ridge either centered over or shifted west and upstream of the flash drought region, leading to broad-scale subsidence and reduced mid-level moisture which acted to limit precipitation development and increase evaporative demand. In addition, several heatwave events developed during the warm season in 2022 and either (1) acted to drive flash drought development via increased evaporative demand or (2) were enhanced by land surface desiccation and land-atmosphere feedbacks following rapid drought intensification. Furthermore, S2S composite forecasts predicted drought development for both events. However, only 20% of the ensembles predicted rapid drought development associated with flash drought for the first event and 16% of the ensembles predicted rapid drought development during the second event. This result highlights a key challenge in S2S prediction of rapidly developing drought conditions versus that of more conventional and slower drought development. The ensembles that did predict rapid drought intensification were associated with the forecasting of positive 500 hPa geopotential height anomalies over the south-central United States (first event) or an amplified wave pattern centered over the west-central United States (second event). Lastly, the compounding effects of two flash droughts in a single warm season led to substantial impacts on agricultural, environmental, and hydrologic sectors across the region.

Warming-induced soil moisture stress threatens food security in India.

Soil moisture (SM) interconnects various components of the Earth system and drives the land-atmosphere feedbacks and food production. However, around 40% of global vegetated land experiences SM drying. India is one of the global hotspots of land-atmosphere interactions and an extensively agrarian economy, but underexplored in terms of SM dynamics and its ramifications on food security. Here, we examine the mechanism of SM drying and its implications on cropland productivity in India based on remote sensing measurements and land surface model simulations in recent decades (2000-2019) and future projection of the 21st century. We find SM reduction predominantly in monsoon (4.5%) and winter (3%) seasons that areinthemajor agricultural seasonsofKharif and Rabi, respectively. Machine learning (ML)-based random forest (RF) reveals that temperature (T, 30.76%) is the dominant driver of SM variability, and then precipitation (P, 26.34%), evapotranspiration (ET, 26.08%) and surface greenness (16.82%). Concurrently, India experiences severe warming in terms of land (0.59 ℃/dec), soil (0.48 ℃/dec) and soil heat flux (SHF, 0.16 W/m2/dec) during 2000-2019. Partial correlation analysis between SM and T limiting the influence of P reveals a strong negative (> - 0.5) relationship in the agriculture intensive regions of Indo-Gangetic Plain (IGP) and South India (SI). Drying owing to warming and increased SHF, termed as warming-induced moisture stress, reduces gross primary productivity (GPP) (i.e. browning) and yield of major food crops of wheat, rain-fed rice, maize and soyabean, predominantly in SI and eastern IGP. Granger Causality shows that warming-induced soil moisture stress has a maximum temporal lag of 1month. In a warming world, the ever-growing population demands more food, and therefore, the warming-induced soil moisture stress is a serious threat to food security in India and similar agro-climatic regions of the world. This calls for climate-resilient agriculture, better agronomic management, improved irrigation and adoption of water-efficient crops.

Observation-constrained projections reveal longer-than-expected dry spells

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10–26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42–44% greater, on average, than ‘mid-range’ or ‘high-end’ future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land–atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Differentiated influences of anomalous subtropical high on extreme persistent precipitation and heatwave events in the Yangtze River Valley

AbstractIn the summers of 2020 and 2022, the Western Pacific Subtropical High (WPSH) intensified extremely and extended westward. However, in summer 2020, the Yangtze River Valley (YRV) witnessed record‐breaking floods, while in 2022, an unprecedented and prolonged heatwave occurred. Distinctly, these two extreme events were caused by different effects of the WPSH: one is enhancement of the transportation of water vapor and the other is adiabatic heating caused by the descending airflow. In June–July 2020, the stable extension of the WPSH ridge line to South China directed a southwesterly airflow along its northwest flank, leading to sustained precipitation in the YRV. Additionally, the midlatitude circulation pattern featured two troughs and two ridges. Such a circulation configuration, combined with the strong and westward WPSH, enabled the continuous southward intrusion of cold air and northward transport of warm moist air, converging over the YRV, and thus influenced extreme persistent precipitation. In contrast, the WPSH covered the YRV almost entirely during summer 2022. Under this influence, the clear‐sky condition and descending airflow through adiabatic warming directly resulted in the heatwave. In addition, local land–atmosphere feedback was crucial in its development and persistence. The soil moisture deficit induced by high temperatures increased the sensible heat flux between the soil and atmosphere upward, further enhanced the surface air temperature and strengthened the heat dry condition.

Comparison of the risks and drivers of compound hot-dry and hot-wet extremes in a warming world

Abstract Compound hot-dry (CHD) and compound hot-wet (CHW) extremes have both intensified under global warming, posing exacerbated socio-economic threats compared to univariate extremes. This study presents a comprehensive assessment and comparison of the historical changes and driving factors behind CHD and CHW using observational data and climate model simulations. Findings indicate a notable surge in CHD and CHW occurrences, with CHW experiencing a higher increasing rate. Our investigation further reveals that anthropogenic climate change predominantly drives the increase in both types of compound extremes, especially for CHW. In contrast, land-atmosphere feedbacks have a limited impact on CHW at a global scale, but substantially contributes to the rise in CHD by reinforcing the negative precipitation-temperature coupling. This influence even surpasses that of anthropogenic climate change in specific regions. Understanding these variations and underlying causes is crucial for improving prediction accuracy and mitigating the impacts of compound extremes.

Dryland self-expansion enabled by land-atmosphere feedbacks.

Dryland expansion causes widespread water scarcity and biodiversity loss. Although the drying influence of global warming is well established, the role of existing drylands in their own expansion is relatively unknown. In this work, by tracking the air flowing over drylands, we show that the warming and drying of that air contributes to dryland expansion in the downwind direction. As they dry, drylands contribute less moisture and more heat to downwind humid regions, reducing precipitation and increasing atmospheric water demand, which ultimately causes their aridification. In ~40% of the land area that recently transitioned from a humid region into a dryland, self-expansion accounted for >50% of the observed aridification. Our results corroborate the urgent need for climate change mitigation measures in drylands to decelerate their own expansion.

Land-atmosphere coupling amplified the record-breaking heatwave at altitudes above 5000 meters on the Tibetan Plateau in July 2022

In July 2022, regions with elevations exceeding 5 000 m on the inner Tibetan Plateau (TP) witnessed a record-breaking heatwave. But how the atmospheric circulation and soil moisture play roles in the occurrence and maintenance of the heatwave in such high elevation climate sensitive region remains unknown. Here, by using the flow analogue method, we find that negative soil moisture anomalies explain more than half of the extreme high temperature during the heatwave, while atmospheric circulation explains less than half. The high soil moisture-temperature coupling metric and the increased correlation between latent heat flux and soil moisture during heatwave revealed strong land-atmosphere feedback in the Qiangtang Plateau which has amplified the heatwave. Analyses of numerical experiments confirm that the presence of interaction between soil moisture and the atmosphere has increased the intensity of hot extreme event under the same atmospheric circulation conditions. Under the warming background, land-atmosphere coupling leads to a faster increase in extreme high temperatures compared to the global mean warming rate, and it is twice as fast as the increase in extreme high temperatures without coupling. We highlight the increased probability of extreme high temperature over the TP in the future due to soil moisture feedback and the results are hoped to inform policymakers in making decisions for climate adaptation activities.

Divergent response of energy exchange to heatwaves from flux tower observations among various vegetation types

Abstract The increasing frequency of European heatwaves and the associated impacts on ecosystems have raised widespread concern during the last two decades. The partitioning of surface energy between latent and sensible heat fluxes plays a pivotal role in regulating heat and water exchange between the land surface and the atmosphere. However, the responses of surface energy partitioning during heatwave events and the contributions of changes in energy partitioning to heatwave development have been underexplored. Here, we investigated the responses of surface energy exchange to temperature extremes during four devastating European heatwaves (2003, 2010, 2018, and 2022) based on long‒term observations from 31 flux towers. Our results demonstrated that the divergent responses of surface energy exchange to heatwaves were modulated by vegetation type and background climate in Europe. Forests maintained similar latent heat fluxes as the climatological mean but largely increased sensible heat under heat‒stressed conditions. While grasslands and croplands tended to increase sensible heat by suppressing latent heat during heatwaves, especially under water‒stressed conditions. Furthermore, the changes in surface energy partitioning strengthened positive land‒atmosphere feedbacks during the heatwave period, leading to unprecedented temperature extremes. This study highlights the importance of surface energy partitioning in land‒atmosphere interactions and heatwave developments.

Land–atmosphere feedbacks weaken the risks of precipitation extremes over Australia in a warming climate

The importance of land–atmosphere feedbacks on regional precipitation changes has been recently noted. However, how land–atmosphere feedbacks shape daily precipitation distributions, particularly the tails of precipitation distributions associated with extreme events, remains unclear on a regional scale. Herein, using the latest land–atmosphere coupling experiments, this study reveals a consistent weakening effect of land–atmosphere feedbacks on the future increase in precipitation extremes over Australia, revealing the most pronounced reduction (56.8%) for the long-term (2080–2099) projection under the low emission (SSP1-2.6) scenario. This weakening effect holds true for shifts in the extreme tail of precipitation distribution, resulting in a reduced risk of precipitation extremes in a warming climate. Land‒atmosphere feedbacks offset 28%–60% of the occurrence risk for the 99th percentile of daily precipitation, with the largest reduction of 172% when precipitation exceeds the 99.7th percentile in the long-term projection under the high emission (SSP5-8.5) scenario. Considering less water replenishment, these feedbacks may reduce the risk of flooding but potentially expedite droughts, highlighting the role of land–atmosphere feedbacks in extreme event projection and regional climate adaptation.

Land-Use Feedback under Global Warming—A Transition from Radiative to Hydrological Feedback Regime

Abstract This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU’s effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback.

Investigating the spatial propagation patterns of meteorological drought events and underlying mechanisms using complex network theory: A case study of the Yangtze River Basin, China

The spatial propagation patterns of meteorological drought events (MDEs) and underlying mechanisms contribute to elucidating and forecasting drought evolution. In this study, gridded MDEs in the Yangtze River Basin (YRB) throughout the entire year, wet season and dry season were extracted from 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) series. Event synchronization (ES) and complex networks (CN) were employed to construct the MDE synchronization networks and MDE spatial propagation networks for various periods. The former were utilized to identify MDE synchronized subregions where MDEs co-occur and co-evolve in the YRB, while the latter were used to quantify the MDE spatial propagation patterns over both the basin and its subregions. The driving mechanisms behind MDE spatial propagation were further investigated by diagnosing the concomitant drought-inducing climate systems. The findings reveal the presence of four MDE synchronized subregions during the wet season and five subregions during the entire year and dry season. These subregions exhibited distinct spatial propagation patterns of MDEs, aligning with overall findings across the YRB. Notable differences were observed between wet and dry seasons, with various subregions exhibiting distinctive spatial propagation patterns during each season. These patterns are driven by variations in the controlling atmospheric circulation systems, leading to anomalies of wind patterns and moisture distribution, ultimately resulting in deficient moisture supply. The variations of tropical sea surface thermal conditions, influences of the Tibetan Plateau and MDE self-propagation triggered by land–atmosphere feedback are considered as three primary influencing factors for MDE spatial propagation in the YRB.

Northern hemisphere land-atmosphere feedback from prescribed plant phenology in CESM

Abstract Plant phenology influences both the terrestrial carbon cycle and land-atmosphere interactions, and therefore can potentially modify large-scale circulations in the atmosphere. However, considerable discrepancies are present among models and between model simulations and observations of plant phenology, adding large uncertainties to future climate projections. Here we modified plant phenology in the Northern Hemisphere in the Community Earth System Model and conducted simulations to characterize how differences in plant phenology influence land-atmosphere coupling. Plant phenology changes the land surface and land-atmosphere interactions by directly modulating absorbed solar radiation and evapotranspiration and indirectly modifying cloud feedback and snow-albedo feedback. Over the Northern Hemisphere, the largest effects occur from March to June when seasonal deciduous phenology is modified from satellite-derived values to model simulations, which results in a >3K increase in surface temperature that propagates to 500hPa (~5km height). Phenology-induced changes in canopy evapotranspiration and surface temperature depend on soil moisture availability during the growing season. Surface temperature decreases significantly due to increasing latent heat flux and cloud reflection where soil moisture is abundant, while soil moisture control over evapotranspiration increases and surface temperature remains little-changed or even increases in more arid regions. Characterizing the influence of phenology on biogeophysical processes is critical, as significant impacts are present both at the land surface and in the atmospheric layers above.

Numerical assessment of changes in land–atmosphere interactions during the rainy season in South America using an updated vegetation map

AbstractLand Use and Land Cover Change (LULCC) is a key driver of changes in land–atmosphere interactions, playing an essential role in climate and weather patterns in South America (SA). This study used a modelling approach to assess the land–atmosphere changes introduced by LULCC over the Brazilian territory. Numerical experiments of early (2006), neutral (2004) and late (2008) rainy season onset years were performed in the Integrated BIosphere Simulator (IBIS) to assess the land‐surface parameters, and in Brazilian Atmospheric Model (BAM) to assess the atmospheric variables. The models were run with a natural (NAT) vegetation map and an updated (UP) vegetation map, which incorporated realistic Brazil deforestation up to the early 2000s. The differences between the two maps were particularly larger over 25°–15°S and 50°–40°W, where the Atlantic Forest and Cerrado biomes were replaced by pasture. IBIS experiments showed that over the area of larger LULCC there was an increase in albedo of up to 8% while reductions in net radiation, surface heat fluxes, surface temperature and surface roughness were noticed. Land–atmosphere feedbacks assessed with BAM experiments showed that LULCC contributed to a drier and colder (stable) atmosphere over central‐east SA that opposes the necessary conditions for the rainy season onset. The changes included increases in surface pressure and low‐level wind associated with reductions in 2 m temperature and surface roughness. These changes contributed to decrease the cloud formation and less precipitation in all three rainy season onset years, but particularly in the late rainy season onset year during September–October–November (SON). The atmospheric changes induced by LULCC show a pattern of dry atmosphere (reduced precipitation), similar to those of late onset years, and indicate that LULCC enhances the late rainy season onset condition over central‐east SA. Additionally, this study highlights the importance of considering up‐to‐date vegetation maps because these changes significantly affect both surface and atmospheric variables.

Influence of lower-tropospheric moisture on local soil moisture–precipitation feedback over the US Southern Great Plains

Abstract. ​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP.

Imprint of urbanization on snow precipitation over the continental USA

Urbanization can alter the local climate through modifications in land-atmosphere feedback. However, a continental scale evaluation of its influence on precipitation phase remains unknown. Here, we assess the difference in the likelihood of snow dominated events (SDEs) over 7,415 urban and surrounding non-urban (buffer) regions across the continental United States. Among 4,856 urban-buffer pairs that received at least five SDEs per year, 81% of urban regions are characterized by a smaller snow probability, 99% by a lower frequency of SDEs, and 57% by faster declining trends in SDEs compared to their buffer counterparts. Notably, urban (buffer) regions with lower snow probability are often characterized by higher net incoming and sensible energy fluxes as compared to buffer (urban) regions, thus highlighting the influence of land-energy feedback on precipitation phase. Results highlight a clear imprint of urbanization on precipitation phase and underscore the need to consider these influences while projecting hydro-meteorological risks.

Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land-atmosphere feedback, thus supplying a unique insight into drought forecasting. Relying on the soil moisture memory, this study investigates the combination of multiple deep-learning modules for sub-seasonal drought indices hindcast in the Huai River basin of China, using long-term ERA5-Land soil moisture records with a noise-assisted data analysis tool. The inter-compared deep-learning models include a hybrid model and a committee machine framework. The results show that the performance of the committee machine framework can be improved with the help of series decomposition and the forecasting skill is not impaired with the lead time increases. Overall, this study highlights the potential of combining deep-learning models with soil moisture memory analysis to improve sub-seasonal drought forecasting.

Persistent warm and cold spells in the Northern Hemisphere extratropics: regionalisation, synoptic-scale dynamics and temperature budget

Abstract. Persistent warm and cold spells are often high-impact events that may lead to significant increases in mortality and crop damage and can put substantial pressure on the power grid. Taking their spatial dependence into account is critical to understand the associated risks, whether in present-day or future climates. Here, we present a novel regionalisation approach of 3-week warm and cold spells in winter and summer across the Northern Hemisphere extratropics based on the association of the warm and cold spells with large-scale circulation. We identify spatially coherent but not necessarily connected regions where spells tend to co-occur over 3-week timescales and are associated with similar large-scale circulation patterns. We discuss the physical drivers responsible for persistent extreme temperature anomalies. Cold spells systematically result from northerly cold advection, whereas warm spells are caused by either adiabatic warming (in summer) or warm advection (in winter). We also discuss some key mechanisms contributing to the persistence of temperature extremes. Blocks are important upper-level features associated with such events – co-localised blocks for persistent summer warm spells in the northern latitudes; downstream blocks for winter cold spells in the eastern edges of continental landmasses; and upstream blocks for winter cold spells in Europe, northwestern North America and east Asia. Recurrent Rossby wave patterns are also relevant for cold and warm spell persistence in many mid-latitude regions, in particular in central and southern Europe. Additionally, summer warm spells are often accompanied by negative precipitation anomalies that likely play an important role through land–atmosphere feedbacks.

Role of atmospheric resonance and land–atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest "Heat Dome" event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.