Land-atmosphere Interactions Research Articles

CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data

Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d−1, unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d−1, mean absolute errors (MAEs) of 0.81 and 0.73 mm d−1, and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.8047038 (Li et al., 2023b).

The role of soil moisture-temperature coupling for the 2018 Northern European heatwave in a subseasonal forecast

This study investigates the predictability of the 2018 Northern Europe heatwave using the GloSea5 forecast model from the perspective of land-atmosphere interactions. We focus on an inverse relationship wherein soil drying leads to increased temperatures and the model's ability to simulate this hypersensitivity in the soil moisture-temperature coupling on the dry side of a breakpoint defined as the soil moisture threshold below which land feedbacks nonlinearly amplify extreme heat. When evaluating forecast model performance in predicting this heatwave, we compare deterministic forecast scores (Hit Rate (HR) and True Skill Score (TSS)) for whether model Surface Soil Moisture (SSM) falls within the hypersensitive regime. GloSea5 exhibits enhanced prediction skill for the extreme heat event when the modelled soil moisture is within the hypersensitive regime. To understand the skill of the heatwave forecast for hit and missed cases of capturing SSM below the breakpoint, we first evaluate the climatological model performance for the water- and energy-limited processes, and then perform a comparison classified by whether SSM verifies on the dry side of the wilting point. The composite analysis demonstrates that the reproducibility of the breakpoint is tied to an improvement in climatological land coupling processes, mainly for classification in the water-limited coupling regime. Therefore, the results suggest that the process-based connection between soil moisture and temperature is a potential source for improving heatwave forecasts on subseasonal to seasonal (S2S) time scales.

Construction and characteristic analysis of background error covariance coupled with land surface temperature

Land surface temperature (LST) is the key variable in land–atmosphere interaction, having an important impact on weather and climate forecasting. However, achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging. This is because there is limited knowledge about the cross-component background error covariance (BEC) between LST and atmospheric state variables. This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables, and whether this relationship varies spatially and temporally. To this end, the BEC coupled with atmospheric variables and LST was constructed (LST-BEC), and its characteristics were analyzed based on the 2023 mei-yu season. The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height, and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature. The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background, and also have obvious diurnal variations. These results provide valuable information for strongly coupled land–atmosphere assimilation.摘要地表温度 (LST) 是涉及陆气相互作用的关键变量, 对天气气候预报具有重要影响. 不过在同化中实现LST与大气的协调分析却并不容易. 这是因为目前对跨陆气圈层的背景误差协方差 (BEC) 的了解较少. 本文旨在探究LST与大气的背景误差是否存在联系, 以及这种联系是否存在时空变化. 为此, 本研究构建了耦合大气变量和LST的BEC, 并基于2023年梅雨季分析了其特征. 总体特征表明: LST误差主要与大气温度相关; 随着大气高度的上升, 误差相关性逐渐减小; LST误差的标准差明显大于低层大气温度. “多雨日”和“少雨日”的时空特征表明, LST与低层大气温湿度误差的相关性及标准差均与天气背景密切相关, 且具有明显的昼夜变化. 上述结果可为后续陆气强耦合同化提供参考.

A novel approach combining satellite and in situ observations to estimate the daytime variation of land surface temperatures for all sky conditions

Land surface temperature (LST) and its diurnal variability are key to understanding the land-atmosphere interactions, hydrological processes and climate change. However, at any given point in time approximately half of the Earth's surface is covered by clouds. This restricts the availability of LST through satellite remote sensing, which works best under clear skies. However, in situ observations continue to monitor atmospheric conditions beneath the clouds that could complement satellite measurements during cloudy conditions. The present study explores a novel approach to estimate hourly LST during the daylight hours using remotely sensed surface solar absorption and in situ observations of daily LST extremes (maximum and minimum) together with an adaptive non-linear fitting approach. A learning algorithm trained against in-situ measurements of LST extrema and diurnal cycle of surface solar absorption together with the associated linear correlation between the two parameters, is used to estimate an optimized set of parameters to approximate hourly LST for each day during the daylight hours between sunrise and sunset. Results show that the method captures the intra-day variability of LST very well under most sky conditions with rms errors below 1.5 K.

Estimating Time-Dependent Structures in a Multivariate Causality for Land–Atmosphere Interactions

Abstract The land surface and atmosphere interaction forms an integral part of the climate system. However, this intricate relationship involves many complicated interactions and feedback effects between multiple variables. As a result, relying solely on traditional linear regression analysis and correlation analysis to distinguish between multivariate complex “driver–response” relations can be challenging, since they do not have the needed asymmetry to establish causality. The Liang–Kleeman (LK) information flow theory provides a strict nonparametric causality measurement for identifying the causality between any given time series, and its recent extension from bivariate to multivariate form provides a powerful tool for causal inference in complex multivariate systems. However, the multivariate LK information flow also assumes stationarity in time and requires a sufficiently long time series to ensure statistical sufficiency. To remedy this challenge, we rely on the square-root Kalman filter to estimate the time-varying form of the multivariate LK information flow causality. The results from theoretical and real-world applications show that the new algorithm provides a valuable tool for characterizing time-varying causal relationships in land–atmosphere interactions, even when the time series are short and highly correlated. Significance Statement Causality in land–atmosphere interactions is generally characterized by seasonal and intraseasonal changes that are usually not captured with commonly used approaches, because most approaches assume the time series are stationary. In this study, we extend the recently proposed multivariate Liang–Kleeman information flow causality (MtvLK) to handle nonstationary systems such as those in land–atmosphere interactions. By considering nonstationarity, we aim to unravel time-varying causal structures that are usually masked out in commonly used methods. Validating the MtvLK with synthetic models showed that the MtvLK is able to obtain the expected causal structures. Furthermore, real-world applications reveal novel findings of the time-varying causal structures between soil moisture, vapor pressure deficit, and the gross primary product.

Cryosphere–Hydrometeorology Observations for a Water Tower Unit on the Tibetan Plateau Using the BeiDou-3 Navigation Satellite System

Abstract Life and civilization in arid regions depend on the availability of freshwater. Arid alpine river basins, where hydrological processes are highly sensitive to rapid warming, act as vital water towers for lowland oases. However, scientific understanding of precipitation variability and related cryosphere–hydrology processes is extremely limited because of the scarcity of in situ observations. The upper Danghe River basin (UDB; ∼14,000 km2) is an arid and westerly dominated basin on the northeastern Tibetan Plateau and is the water source for the Dunhuang Oasis in China. We have established a comprehensive cryosphere–hydrometeorology observation network in the basin since 2014. At present, the network consists of 21 automatic rain gauges, 22 soil freeze–thaw monitoring stations, 4 automatic weather stations (AWS), and a 50-m gradient meteorological tower with an eddy covariance system. In particular, the 18 sites, located in remote areas without public networks, are equipped with new-generation BeiDou-3 communication terminals that enable the observations to be easily, safely, and reliably read and quality controlled in near–real time from offices in the city or at home. This integrated observation network over the UDB that facilitates the monitoring of cryosphere–hydrology processes, land–atmosphere interactions, and local weather processes. In addition, the observations are helpful for the objective evaluation, and continual improvement, of hydrological models, satellite-retrieval products, and reanalysis datasets. Finally, the network is expected to promote a better understanding of the status and role of water towers in arid zones and to provide basic data support for the sustainable development of the Dunhuang Oasis and the Belt and Road.

Modeling Land–Atmosphere Interactions over Semiarid Plains in Morocco: In-Depth Assessment of GCM Stretched-Grid Simulations Using In Situ Data

Abstract Land surface–atmosphere interactions are a key component of climate modeling. They are particularly critical to understand and anticipate the climate and the water resources over the semiarid and arid North African regions. This study uses in situ observations to assess the ability of the IPSL-CM global climate model to simulate the land–atmosphere interactions over the Moroccan semiarid plains. A specific configuration with a grid refinement over the Haouz Plain, near Marrakech, and nudging outside Morocco has been performed to properly assess the model’s performances. To ensure reliable model–observation comparisons despite the fact that station measurements are not representative of a mesh-size area, we carried out experiments with adapted vegetation properties. Results show that the CMIP6 version of the model’s physics represents the near-surface climate over the Haouz Plain reasonably well. Nonetheless, the simulation exhibits a nocturnal warm bias, and the wind speed is overestimated in tree-covered meshes and underestimated in the wheat-covered region. Further sensitivity experiments reveal that LAI-dependent parameterization of roughness length leads to a strong surface wind drag and to underestimated land surface atmosphere thermal coupling. Setting the roughness heights to the observed values improves the wind speed and, to a lesser extent, the nocturnal temperature. A low bias in latent heat flux and soil moisture coinciding with a pronounced diurnal warm bias at the surface is still present in our simulations. Including a first-order irrigation parameterization yields more realistic simulated evapotranspiration flux and daytime skin surface temperatures. This result raises the importance of accounting for the irrigation process in present and future climate simulations over Moroccan agricultural areas.

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Abstract. Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

The impacts of rising vapour pressure deficit in natural and managed ecosystems.

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growthand survival. These responses are exacerbated by land-atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resourcesand crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co-evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigationand management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.

A New Post‐Hoc Method to Reduce the Energy Imbalance in Eddy Covariance Measurements

AbstractLatent and sensible heat flux observations are essential for understanding land–atmosphere interactions. Measurements from the eddy covariance technique are widely used but suffer from systematic energy imbalance problems, partly due to missing large eddies from sub‐mesoscale transport. Because available energy drives the development of large eddies, we propose an available energy based correction method for turbulent flux measurements. We apply our method to 172 flux tower sites and show that we can reduce the energy imbalance from −14.99 to −0.65 W m−2 on average, together with improved consistency between turbulent fluxes and available energy and associated increases in r2 at individual sites and across networks. Our results suggest that our method is conceptually and empirically preferable over the method implemented in the ONEFlux processing. This can contribute to the efforts in understanding and addressing the energy imbalance issue, which is crucial for the evaluation and calibration of land surface models.

Improving Estimates of Land–Atmosphere Coupling Through a Novel Framework of Land Aridity Classification

AbstractThe evapotranspiration (ET) regime is an illustration of the water‐energy interactions at the land surface and is important for regional climate. In this study, we propose a revised framework for land aridity classification based on the ET regime and the conditional mutual information method. The proposed framework effectively captures the influences of short‐term water and energy supply on ET and highlights their potential roles in extreme events. This framework can be a supplement to the traditional classification schemes that rely on long‐term mean climate. Furthermore, we examine the sensitivity of this land aridity framework to different model physics parameterization schemes. The cumulus schemes are found to have the most important impact, followed by the radiation and microphysics schemes, while the planetary boundary layer schemes have the weakest impact. These findings provide valuable insights for the identification of land aridity and its relationship with land–atmosphere interactions.

Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Abstract. We present a comprehensive observational approach that aims to establish relationships between the surface–atmosphere exchange of ammonia (NH3) and CO2 uptake and transpiration by vegetation. In doing so, we study relationships useful for the improvement and development of NH3 flux representations in models. The NH3 concentration and flux are measured using a novel open-path miniDOAS (differential optical absorption spectroscopy) measurement setup, taken during the 5-week Ruisdael Land–Atmosphere Interactions Intensive Trace-gas and Aerosol measurement (RITA-2021) campaign (25 August until 12 October 2021) at the Ruisdael Observatory in Cabauw, the Netherlands. After filtering for unobstructed flow, sufficient turbulent mixing and CO2 uptake, we find the diurnal variability in the NH3 flux to be characterized by daytime emissions (0.05 µgm-2s-1 on average) and deposition at sunrise and sunset (−0.05 µgm-2s-1 on average). We first compare the NH3 flux to the observed gross primary production (GPP), representing CO2 uptake, and latent heat flux (LvE), representing net evaporation. Next, we study the observations following the main drivers of the dynamic vegetation response, which are photosynthetically active radiation (PAR), temperature (T) and the water vapor pressure deficit (VPD). Our findings indicate the dominance of the stomatal emission of NH3, with a high correlation between the observed emissions and both LvE (0.70) and PAR (0.72), as well as close similarities in the diurnal variability in the NH3 flux and GPP. However, efforts to establish relationships are hampered by the high diversity in the NH3 sources of the active agricultural region and the low data availability after filtering. Our findings show the need to collocate meteorological, carbon and nitrogen studies to advance our understanding of NH3 surface exchange and its representation.

Influence paradigms of soil moisture on land surface energy partitioning under different climatic conditions

Soil moisture (SM) directly controls the land surface energy partition which plays an important role in the formation of extreme weather events. However, its dependence on specific climatic conditions is not thoroughly understood due to the complexity of soil moisture effects. Here, we examine the relationship between SM and surface energy partitioning under different climate conditions, and identify the influence paradigms of soil moisture on surface energy partition. We find that temperature changes can explicitly determine the impact paradigm of different physical processes, i.e. evapotranspiration, soil freezing and thawing, and such influence paradigms are also affected by atmospheric aridity (VPD). Globally, there are five paradigms that effects on surface energy partitioning, including the warm-wet paradigm (WW), transitional paradigm (TP), warm-dry paradigm (WD), cool-wet paradigm (CW) and cold paradigm (CP). Since 1981, the global area proportion for TP is observed to increase pronouncedly. We also find that the critical SM threshold exhibits regional variations and the global average is 0.45 m3/m3. The identified paradigms and their long-term change trends provide new insights into the global intensification of land-atmosphere interaction, which has important implications for global warming and the formation of heatwaves.

AFM Special Issue Summary - Integrating Surface Flux with Boundary Layer Measurements

To help bridge science topics related to land-atmosphere interactions, we organized a virtual special issue in this journal (Agricultural and Forest Meteorology [AFM]) entitled, “Land-Atmosphere Interactions: Integrating Surface Flux with Boundary Layer Measurements.” The motivation for the special issue was driven by existing disciplinary barriers between research areas that all address land-atmosphere interactions. In particular, it addressed research silos between those who study features of the land surface, surface fluxes (including water, energy, and trace gases), atmospheric boundary layer growth and thermodynamics, and atmospheric composition and aerosols. The special issue sought to bring these communities together to integrate multiple observations across the soil-vegetation-atmosphere continuum with the aim of 1) improving broader understanding of land-atmosphere interactions, feedbacks, and coupling, 2) fostering new collaborations between atmospheric and surface flux scientists, and 3) identifying new paths for integrative research. Here, we provide an overview and synthesis of the special issue.

Research into land atmosphere interactions supports the sustainable development agenda

Abstract Non-technical summary Greenhouse gas emissions and land use change – from deforestation, forest degradation, and agricultural intensification – are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specializing in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land–atmosphere interaction research and its contribution to the sustainable development agenda. Technical summary Greenhouse-gas emissions from human activities and land use change (from deforestation, forest degradation, and agricultural intensification) are contributing to climate change and biodiversity loss. Afforestation, reforestation, or growing bioenergy crops (with carbon capture and storage) are important land-based strategies to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The effectiveness of these actions depends on terrestrial ecosystems and their role in controlling or moderating the exchange of water, heat, and chemical compounds between the land surface and the atmosphere. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS), a global research network of Future Earth, enables the international community to communicate and remain up to date with developments and concepts about terrestrial ecosystems and their role in global water, energy, and biogeochemical cycles. Covering critically important topics such as fire, forestry, wetlands, methane emissions, urban areas, pollution, and climate change, the iLEAPS Global Research Programme sits center stage for some of the most important environmental questions facing humanity. In this paper, we outline the new challenges and opportunities for land–atmosphere interaction research and its role in supporting the broader sustainable development agenda. Social Media Summary Future directions for research into land–atmosphere interactions that supports the sustainable development agenda

Spatiotemporal analysis of seasonal trends in land surface temperature within the distribution range of Moringa peregrina (Forssk.) in Southern and Southeastern Iran.

Temperature fluctuations and related factors are among the main causes of climate change. Understanding the temporal and spatial variations in temperature can shed light on how species respond to climate change. Plants generally persist in suitable microclimates in response to environmental change; however, examining long-term temperature variations within a species' range can be challenging using field observations. Thermal remote sensing, on the other hand, provides multi-scale time-series data with good coverage and regularity to overcome the challenges associated with field observations in environmental monitoring. Although changes in land surface temperature (LST) affect climate, hydrological processes, land-atmosphere interactions, and ecological activities, this metric has not received much research attention. This study aimed to analyze changes in habitat suitability and microclimatic conditions for Moringa peregrina. Seasonal changes in LST within the distribution range of the species were also investigated. To this aim, mean seasonal LST was computed in Google Earth Engine using the daily MODIS/006/MYD13A2 product from 2003 to 2023. Subsequently, a binary habitat suitability map was created based on the true skill statistic (TSS). The Mann-Kendall test was used to analyze seasonal LST trends. Major trends in LST were quantified based on the z-score, and compatibility with habitat suitability was evaluated via GAP analysis and the Kappa index. Seasonal temperature trends were evaluated by comparing each season with the following season using binary comparison. Landforms at presence points were regarded as microclimates and the sensitivity of microclimates to LST was evaluated using two methods: Principal component analysis (PCA) was used to quantify seasonal LST heterogeneity and the random forest (RF) approach was used to evaluate the effect of environmental parameters on habitat suitability within microclimates. The Kappa index revealed a weak overlapping between suitable / unsuitable habitat and the surfaces affected by the trend of changes. Moreover, the suitable habitat of Moringa peregrina in spring, autumn and winter is spatially overlapped by areas that have shown an increasing LST trend, and the presence points have not experienced an increasing temperature trend only in the summer. The findings show that the analysis of seasonal trends in LST provides insights into the effect of LST on habitat suitability and the condition of vegetation. The current study clearly shows that seasonal changes have had a significant impact on the distribution and habitat suitability of M. peregrina, particularly during summer and winter. Improved habitat suitability and range expansion were observed throughout the year. The study also highlights the role of landforms in regulating temperature. Landforms such as local ridges with minimal temperature fluctuations and regions near the Oman Sea were identified as potential future habitats due to favorable humidity conditions.

Characterizing extreme drought and wetness in Guangdong, China using global navigation satellite system and precipitation data

As global temperature rises, the frequency of extreme climate events, e.g., severe droughts and floods, has increased significantly and caused severe damage over the past years. To this regard, precipitation efficiency, a crucial meteorological parameter, could provide valuable insights for a better understanding of the patterns and characteristics of these extreme events. In this study, taking Guangdong province as an exemplary region, we first obtained long-term and high-resolution historical records of precipitation efficiency by integrating the observations from a dense network of Global Navigation Satellite System (GNSS) stations with precipitation data, and then characterized the extreme drought and wetness through climate indices. We found a distinct seasonal trend in precipitation efficiency in Guangdong, with annual fluctuations ranging from 10 to 25%. Notably, precipitation efficiency is higher in proximity to the Pearl River Delta Plain and gradually decreases towards the east and west. The occurrence of anomalous peaks and valleys in precipitation efficiency generally corresponds to dry and wet conditions, respectively. A total of 9 extreme wet events and 6 dry events occurred from January 2007 to May 2022, with durations from 3 to 6 months. Our results also demonstrated that both wet and dry frequencies exhibit an increasing trend with the expansion of the time scale, and the frequency of extreme events near the Pearl River Delta Plain surpasses that of other regions. Furthermore, the propagation time from meteorological anomalies to agricultural and hydrological anomalies is about 3 months. The periodic characteristics of meteorological anomalies are identified as the primary driver for other anomalous periodic patterns. Our work unveils the long-term dynamic behavior of precipitation efficiency, as well as the characteristics of extreme drought and wetness events in the regions characterized by intricate land–atmosphere interactions.

Uncertainty reduction for precipitation prediction in North America.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization

Abstract. Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than −4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization.

Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2

Abstract. In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.