Land-atmosphere Feedbacks Research Articles

Environmental factors controlling soil warming and wetting during 2000–2020 in permafrost and non-permafrost regions across the Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau (QTP) has experienced rapid environmental changes, including climate warming and wetting, since the 1980s. These environmental changes significantly impact the shallow soil hydrothermal conditions, which have key roles in land–atmosphere feedback and ecosystem functions. However, the spatial variations and responses of soil hydrothermal conditions to environmental changes over the QTP with permafrost (PF) and seasonal frost (SF) remain unclear. In this study, we investigated the spatial variations in soil temperature (ST) and soil moisture (SM) changes over the QTP from 2000 to 2020 using 99 in-situ sites with observations at 4 depths (i.e. 10, 40, 100 and 200 cm). The main environmental controlling factors were further identified using a calibrated statistical model. Results showed that significant (p < 0.05) soil warming occurred at multiple soil layers during 2000–2020 with a wide variation (i.e. 0.033–0.039 °C per year on average), whereas the warming rates at PF sites were two times greater than those at SF sites. In addition, the soil wetting rate was high over the SF region, whereas the soil wetting rate was low over the PF region. Aside from air temperature, changes in thawing degree days and solar radiation (Srad) contributed most to soil warming in the PF region, whereas changes in rainfall, Srad and evaporation (EVA) have been identified as the key factors in the SF region. As for soil wetting, changes in snowfall, freezing degree days and vegetation have noticeable nonlinear effects over the PF region, whereas changes in EVA, Srad and rainfall highlighted distinct linear and nonlinear effects in the SF region. These findings enhance our understanding of the hydrothermal impacts of future environmental changes over the QTP.

Shift of soil moisture-temperature coupling exacerbated 2022 compound hot-dry event in eastern China

Compound hot-dry events (CHDEs) are among the deadliest climate hazards and are occurring with increasing frequency under global warming. The Yangtze River Basin in China experienced a record-breaking CHDE in the summer of 2022, causing severe damage to human societies and ecosystems. Recent studies have emphasized the role of atmospheric circulation anomalies in driving this event. However, the contribution of land–atmosphere feedback to the development of this event remains unclear. Here, we investigated the impacts of soil moisture-temperature coupling on the development of this concurrent heatwave and drought. We showed that large amounts of surface net radiation were partitioned to sensible heat instead of latent heat as the soil moisture-temperature coupling pattern shifted from energy-limited to water-limited under low soil moisture conditions, forming positive land–atmosphere feedback and leading to unprecedented hot extremes in August. The spatial heterogeneity of hot extremes was also largely modulated by the land–atmosphere coupling strength. Furthermore, enhanced land–atmosphere feedback has played an important role in intensifying CHDEs in this traditional humid region. This study improves the understanding of the development of CHDEs from three aspects, including timing, intensity, and spatial distribution, and enables more effective early warning of CHDEs.

Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas

Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous regions. It calls for further efforts to improve the representation of subgrid-scale processes affecting snowpack in climate models.

To what extent can the ozone valley over the Tibetan Plateau influence the East Asian summer precipitation?

The ozone valley over the Tibetan Plateau (OVTP) has experienced significant interannual variations during the past decades. Previous studies have primarily focused on the origins of OVTP rather than its climate impact. This study reveals that OVTP during its peak season (May–July) explains up to 15% of the summer precipitation variability in East Asia. The results suggest that the surface temperature (Ts) anomaly over the Tibetan Plateau (TP) acts as a link between OVTP and East Asian precipitation. Through the positive land-atmosphere feedback, the Ts anomaly over the TP is amplified. The anomalous Ts pattern persists into summer (June–August) due to the land memory effect and impacts the East Asian precipitation by modulating the local circulation. The Specified-Chemistry version of the Whole Atmosphere Community Climate Model is employed to validate that MJJ OVTP results in a substantial increase of Ts over TP and induces an anomalous anti-cyclone centered over the Yangtze-Huaihe River Basin during summer. Consequently, negative precipitation anomalies are observed in the Yangtze River Basin, while positive precipitation anomalies occur in Southern China. The linear baroclinic model further demonstrates that the diabatic heating over the TP serves as the link between MJJ OVTP and East Asian summer precipitation patterns. Our analysis of Coupled Model Intercomparison Project Phase 6 models reveals that a more accurate prediction of East Asian precipitation requires an improved understanding of the relationship between OVTP and TP Ts.

River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon

Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

The Representation of Soil Moisture-Atmosphere Feedbacks across the Tibetan Plateau in CMIP6

Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture–driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture–atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture-atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components.Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture–atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture–driven predictability. To improve the representation of land–atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes.

Doppler Lidar Measurements of Wind Variability and LLJ Properties in Central Oklahoma during the August 2017 Land–Atmosphere Feedback Experiment

Abstract Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s−1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s−1. The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s−1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height. Significance Statement Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.

Drivers of accelerated warming in Mediterranean climate-type regions

The near-surface temperature in Mediterranean climate-type regions has increased overall similarly or more rapidly than the global mean rates. Although these regions have comparable climate characteristics and are located at similar latitudes, recent warming acceleration is most pronounced in the Mediterranean Basin. Here, we investigate the contributions of several climate drivers to regional warming anomalies. We consider greenhouse gases, aerosols, solar irradiance, land–atmosphere interactions, and natural climate variability modes. Our results highlight the dominant role of anthropogenic greenhouse gas radiative forcing in all Mediterranean climate-type regions, particularly those in the northern hemisphere. In the Mediterranean Basin, the recent warming acceleration is largely due to the combined effect of declining aerosols and a negative trend in near-surface soil moisture. While land-atmosphere feedbacks are also important in other locations (e.g., California and Southern Africa), this synergy is unique in the Mediterranean Basin. These two regional climate drivers have natural and anthropogenic components of equivalent importance. Such feedbacks are not fully resolved in the current regional climate projections.

Irrigation in the Earth system

Irrigation accounts for ~70% of global freshwater withdrawals and ~90% of consumptive water use, driving myriad Earth system impacts. In this Review, we summarize how irrigation currently impacts key components of the Earth system. Estimates suggest that more than 3.6 million km2 of currently irrigated land, with hot spots in the intensively cultivated US High Plains, California Central Valley, Indo-Gangetic Basin and northern China. Process-based models estimate that ~2,700 ± 540 km3 irrigation water is withdrawn globally each year, broadly consistent with country-reported values despite these estimates embedding substantial uncertainties. Expansive irrigation has modified surface energy balance and biogeochemical cycling. A shift from sensible to latent heat fluxes, and resulting land–atmosphere feedbacks, generally reduce regional growing season surface temperatures by ~1–3 °C. Irrigation can ameliorate temperature extremes in some regions, but conversely exacerbates moist heat stress. Modelled precipitation responses are more varied, with some intensive cropping regions exhibiting suppressed local precipitation but enhanced precipitation downstream owing to atmospheric circulation interactions. Additionally, irrigation could enhance cropland carbon uptake; however, it can also contribute to elevated methane fluxes in rice systems and mobilize nitrogen loading to groundwater. Cross-disciplinary, integrative research efforts can help advance understanding of these irrigation–Earth system interactions, and identify and reduce uncertainties, biases and limitations. Irrigation accounts for a substantial proportion of global water usage and can have biophysical and biogeochemical impacts on Earth systems. This Review outlines key irrigation–Earth system interactions, and discusses the effect of future climate and socioeconomic changes on irrigation patterns and their interaction.

Surface‐driven amplification of Madden–Julian oscillation circulation anomalies across East Africa and its influence on the Turkana jet

AbstractIn semi‐arid environments, rainfall‐driven soil moisture fluctuations exert a strong influence on surface turbulent fluxes. Intraseasonal rainfall variability can therefore impact low‐level atmospheric temperatures and influence regional circulations. Using satellite observations and an atmospheric reanalysis, we investigate whether rainfall variability induced by the Madden–Julian oscillation (MJO) triggers land–atmosphere feedbacks across East Africa.We identify that surface fluxes during the East African wet seasons (March–May and October–December) are sensitive to MJO‐induced precipitation variations across low‐lying regions of South Sudan and highland regions of Uganda and southwest Kenya. For example, during MJO phases 6 to 8, when rainfall is suppressed, surface temperatures and sensible heat fluxes increase, whilst evapotranspiration decreases. Spatial variations in the surface flux response to rainfall variability feed back onto the atmosphere through amplifying MJO‐associated pressure anomalies. During dry MJO events for instance, surface warming across the exit region of the Turkana channel increases the low‐tropospheric along‐channel pressure gradient and intensifies the Turkana jet. We conclude that average surface‐driven temperature fluctuations during a single day are responsible for approximately 12% of MJO‐associated variability of the Turkana jet speed. However, we expect that the accumulation of heat over multiple days to the west of the East African Highlands further amplifies anomalies in the pressure gradient and jet intensity. Modelling experiments are required to quantify the accumulated impact of the surface forcing. Surface‐driven Turkana jet variations influence the East African moisture budget and affect the intensity and inland propagation of coastal convection. Not only is this the first study to investigate the importance of intraseasonal land–atmosphere feedbacks across East Africa, but it is also the first to show that Turkana jet characteristics are partly driven by surface conditions. This work motivates an investigation into whether subseasonal forecast models fully harness the predictability from surface‐induced jet variations.

Disentangling the Role of Forest Structure and Functional Traits in the Thermal Balance of the Mediterranean–Temperate Ecotone

AbstractThe thermal balance of forests is the result of complex land–atmosphere interactions. Different climate regimes and plant functional types can have contrasting energy budgets, but little is known about the influence of forest structure and functional traits. Here, we combined spaceborne measurements of surface temperature from ECOSTRESS with ground‐based meteorological data to estimate the thermal balance at the surface (∆Tcan−air) during four summers (2018–2021), at the Mediterranean–temperate ecotone in the NE Iberian Peninsula. We analyzed the spatiotemporal drivers of ∆Tcan−air by quantifying the effects of meteorology, forest structure (stand density, tree height) and ecophysiology (hydraulic traits), during normal days and hot spells. Canopy temperatures (Tcan) fluctuated according to changes in air temperature (Tair) but were on average 4.2 K warmer. During hot spells, ∆Tcan−air was smaller than during normal periods. We attribute this decrease to the advection of hot and dry air masses from the Saharan region resulting in a sudden increase in Tair relative to Tcan. Vapor pressure deficit (VPD) was negatively correlated with ∆Tcan−air, since the highest VPD values coincided with peaks in heat advection. Nonetheless, Tcan increased with VPD due to decreased transpiration (following stomatal closure), even though sufficient soil water availability enabled some degree of evaporative cooling. Our findings demonstrate that plot‐scale forest structural and hydraulic traits are key determinants for the forest thermal balance. The integration of functional traits and forest structure over relevant spatial scales would improve our ability to understand and model land–atmosphere feedbacks in forested regions.

Using the U.S. Climate Reference Network to Identify Biases in Near- and Subsurface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model

Abstract 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. Significance Statement Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.

Land–atmosphere feedbacks contribute to crop failure in global rainfed breadbaskets

Global crop yields are highly dependent on climate variability, with the largest agricultural failures frequently occurring during extremely dry and hot years. Land–atmosphere feedbacks are thought to play a crucial role in agricultural productivity during such events: precipitation deficits cause soil desiccation, which reduces evaporation and enhances sensible heating from the land surface; the amplified local temperatures and moisture deficits can be detrimental to crop yield. While this impact of local land–atmosphere feedbacks on agricultural productivity has recently been reported, the dependency of crop yields on upwind regions remains understudied. Here, we determine the spatio-temporal origins of moisture and heat over the world’s largest 75 rainfed breadbaskets, and illustrate the crop yield dependency on upwind regions. Further, we disentangle the role of local and upwind land–atmosphere interactions on anomalous moisture and heat transport during low-yield years. Our results indicate that crop failure increases on average by around 40% when both upwind and local land–atmosphere feedbacks cause anomalously low moisture and high heat transport into the breadbaskets. The impact of upwind land–atmosphere feedbacks on productivity deficits is the largest in water-limited regions, which show an increased dependency on moisture supply from upwind land areas. Better understanding these upwind–downwind dependencies in agricultural regions can help develop adaptation strategies to prevent food shortage in a changing climate.

Anthropogenic Forcing Decreased Concurrent Soil Drought and Atmospheric Aridity in the Historical Period 1850–2013

AbstractAs awareness of the importance of anthropogenic climate change has increased, attention is being focused on “compound extremes,” such as co‐occurring soil drought (low amounts of soil moisture, SM) and atmospheric aridity (high vapor‐pressure deficits, VPD), because of the disproportionate impacts of such extreme conditions on natural and societal systems. Few advances, however, have been made in isolating the net effect of anthropogenic forcing on the occurrence of such compound extremes. Nine Earth System models (ESMs) under natural‐only and fully‐forced simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) indicated that the presence of anthropogenic forcing in the historical period 1850–2013 weakened the coupling between SM and VPD and thus decreased the frequency of compound drought and aridity globally. The anthropogenically induced decrease in the strength of land–atmospheric feedbacks, which offset the drying trend effects of global warming by countering the expected natural correlation between SM and VPD, appears to have been responsible for the relative rarity of concurrent drought and aridity in the historical period. We also modeled independently the anthropogenic forcings of aerosols and greenhouse gases (GHGs) to further highlight that the widespread weakening of land–atmospheric feedbacks may have been associated primarily with the cooling effects induced by increases in anthropogenic emissions of aerosols, because the increase in intensity and frequency of compound drought and aridity that might have been expected from rising GHG concentrations was widely counter‐balanced until very recently by an aerosol‐driven cooling, particularly for the middle and high latitudes of the Northern Hemisphere. This finding indicates that the trade‐off between these two main anthropogenic forcings may determine future patterns of concurrent drought and aridity in a changing climate. If global emissions of anthropogenic aerosols decrease in the future, as expected, our results imply a renewed strengthening of land–atmospheric feedbacks, and thus an intensification of concurrent drought and aridity.

Elevation-dependent intensification of fire danger in the western United States

Studies have identified elevation-dependent warming trends, but investigations of such trends in fire danger are absent in the literature. Here, we demonstrate that while there have been widespread increases in fire danger across the mountainous western US from 1979 to 2020, trends were most acute at high-elevation regions above 3000 m. The greatest increase in the number of days conducive to large fires occurred at 2500–3000 m, adding 63 critical fire danger days between 1979 and 2020. This includes 22 critical fire danger days occurring outside the warm season (May–September). Furthermore, our findings indicate increased elevational synchronization of fire danger in western US mountains, which can facilitate increased geographic opportunities for ignitions and fire spread that further complicate fire management operations. We hypothesize that several physical mechanisms underpinned the observed trends, including elevationally disparate impacts of earlier snowmelt, intensified land-atmosphere feedbacks, irrigation, and aerosols, in addition to widespread warming/drying.

How does the explicit treatment of convection alter the precipitation–soil hydrology interaction in the mid-Holocene African humid period?

Abstract. Global climate models with coarse horizontal resolution are largely unable to reproduce the monsoonal precipitation pattern over North Africa during the mid-Holocene. Here we present the first regional, storm-resolving simulations with an idealized but reasonable mid-Holocene vegetation cover. In these simulations, the West African monsoon expands farther north by about 4–5∘, and the precipitation gradient between the Guinea coast and the Sahara decreases compared to simulations with a barren Sahara as it is today. The northward shift of monsoonal precipitation is caused by land surface–atmosphere interaction, i.e., the coupling of soil moisture and precipitation, as well as interactions of the land surface with the large-scale monsoon circulation (e.g., the African easterly jet). The response of the monsoon circulation to an increased vegetation cover is qualitatively similar but more pronounced in parameterized convection simulations. We attribute the differences in monsoonal precipitation to differences in soil moisture that are strongly controlled by runoff and precipitation characteristics. If precipitation is intense and falls over a spatially small region, as in our storm-resolving simulations, about 35 % of all precipitation water goes into runoff instead of filling soil moisture storage. In contrast, in light and spatially more homogeneous precipitation, as produced in our parameterized convection simulations, only some 20 % leaves the grid cell as runoff. Therefore, much more water is available to maintain high soil moisture content. We confirm the significant role of soil moisture and runoff by performing simulations with the same constant soil moisture field in both storm-resolving and parameterized convection simulations. These constant soil moisture simulations cancel the effect of lower soil moisture on the land–atmosphere feedback cycle in our storm-resolving simulations. We show that precipitation strongly increases in the storm-resolving simulations, especially in moisture-controlled regions, such as the northern Sahel and Sahara, and reaches equally high values as in parameterized convection simulations. Our study highlights how the type of rainfall (e.g., local and intense or widespread and light) impacts soil moisture and thus land–atmosphere feedbacks. This is contrary to many studies that focus mainly on the amount of rainfall and how it modifies land–atmosphere feedbacks. Moreover, this study suggests that comprehensive land-surface schemes, which appropriately respond to varying precipitation characteristics, are needed for studying land surface–atmosphere interaction.

Coupled and Stand-Alone Regional Climate Modeling of Intensive Storms in Western Canada

A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called weather research forecasting (WRF) was coupled with a land-surface scheme (Noah) to simulate intensive storms in Alberta of Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land-surface temperature influence latent and sensible heat fluxes and modulate both thermal and dynamical characteristics of land and the lower atmosphere. WRF was set up in a two-way, 3-domain nested framework so that the output of the outermost domain (D1) was used to run the 2nd domain (D2), and the output of D2 was used to run the innermost, 3rd domain (D3). By this 2-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from global climate models (GCMs) for the baseline period 1980–2005. A regional frequency analysis and a quantile-quantile bias correction method were applied to develop intensity-duration-frequency (IDF) curves using precipitation data simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agrees well with observed data from a 13-rain gauge network of the City of Edmonton. The 5th-generation mesoscale atmospheric model (MM5) of National Center for Atmospheric Research was also set up in a 3-domain, but 1-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by special reports on emission scenario climate scenarios of coupled model inter-comparison project (CMIP3) and WRF-Noah driven by representative concentration pathway climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s and to the 2080s.

Soil moisture-evaporation coupling shifts into new gears under increasing CO2

When soil moisture (SM) content falls within a transitional regime between dry and wet conditions, it controls evaporation, affecting atmospheric heat and humidity. Accordingly, different SM regimes correspond to different gears of land-atmosphere coupling, affecting climate. Determining patterns of SM regimes and their future evolution is imperative. Here, we examine global SM regime distributions from ten climate models. Under increasing CO2, the range of SM extends into unprecedented coupling regimes in many locations. Solely wet regime areas decline globally by 15.9%, while transitional regimes emerge in currently humid areas of the tropics and high latitudes. Many semiarid regions spend more days in the transitional regime and fewer in the dry regime. These imply that a larger fraction of the world will evolve to experience multiple gears of land-atmosphere coupling, with the strongly coupled transitional regime expanding the most. This could amplify future climate sensitivity to land-atmosphere feedbacks and land management.

Soil moisture revamps the temperature extremes in a warming climate over India

Soil moisture (SM) plays a crucial role in altering climate extremes through complex land-atmosphere feedback processes. In the present study, we investigated the impact of SM perturbations on temperature extremes (ExT) over India for the historical period (1951–2010) and future climate projection (2051–2100) under 4 K warming scenario. We note that more than 70% area of the Indian landmass has experienced significant changes in characteristics of ExT due to SM perturbations. In particular, we see larger impact of SM perturbations on ExT over the north-central India (NCI), which is a hotspot of strong SM-temperature coupling. Over NCI, a 20% departure in SM significantly revamps frequency, duration and intensity of ExT by 2–5 events/year, 1-2 days/event and 0.5–2.1 °C, respectively, through modulating surface energy partitioning, evapotranspiration and SM memory. Importantly, the impact of SM perturbations on frequency and duration of ExT events becomes less prominent with intensification of global warming.