Land Atmosphere Coupling Strength Research Articles

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.

Contrasting Land and Atmospheric Controls on the Generalized Complementary Relationship of Evaporation Over Grasslands and Forests

AbstractThe generalized complementary relationship (CR) rooted on land–atmosphere coupling has been widely used to study evaporation over various ecosystems. Evaporation estimation models based on CR usually contain two parameters. The first one controls the shape of CR, and the second one is related to potential evaporation (Epo). Previous studies have found substantial variations in these parameters, but how they are affected by land surface and/or the atmosphere under conditions with varying land–atmosphere coupling remains unclear. In this study, the land and atmospheric controls for the generalized CR were comparatively investigated focusing on the two parameters of the sigmoid type function by using the data of 24 grassland and 19 forest flux sites and by considering the contrasting land–atmosphere coupling strength. The strong coupling to the outer atmosphere at the forest sites resulted in a large variation in the Epo‐related parameter and its significant correlation with the mean relative humidity. The shape parameter had a large variation at the grassland sites due to the weak coupling with the outer atmosphere and was significantly correlated with the mean soil water content. The simple parameterizations for the two parameters based on the correlations improved the predictions of monthly actual evaporation compared with the parameterizations for the fixed parameters corresponding to ecosystem types. The effects of land surface wetness and local advections on the generalized CR over grasslands and forests were also discussed respectively.

Contributions of Local Land‒Atmosphere Coupling and Mesoscale Atmospheric Circulation to the 2013 Extreme Flash Drought and Heatwave Compound Event Over Southwest China

AbstractThe flash drought and heat wave compound event of the 2013 summer in Southwest China was exceptional, with profound socio‒economic impacts. Existing studies have captured the signal of this compound event; however, its causes are still poorly understood. In this study, we utilized a high‐resolution index to examine the life cycle of this compound event from 1980 to 2020 and quantified the relative contributions of land‒atmosphere coupling and mesoscale atmospheric circulation using the air parcel backward tracking method. It was found that the dry soil caused by the precipitation deficit further inhibited precipitation and caused the soil to become even drier, which then led to the development of the flash drought. The dry soil induced a substantial increase in the land‒atmosphere sensible heat flux and a rise in the near‐surface air temperature. During this process, the strength of land‒atmosphere coupling was 2.5 standard deviations higher than the mean value of the climate, playing an integral role in the near‐surface air temperature increase, accounting for 55.9% of the maximum temperature changes. The mesoscale atmospheric circulation contributed 44.1%, of which horizontal advection accounted for 41.1%, while vertical advection only accounted for 3.0%. The western Pacific subtropical high expanded westward, accompanied by the northward‐moving westerly jet stream, which prevented cold air from accessing the Southwest China, and abnormal subsidence interfered with precipitation formation. Furthermore, the control of an abnormally high‐pressure system over the region led to a decrease in cloud cover, allowing more surface incident solar radiation and contributing to the increase in the near‐surface air temperature.

Potential of remote sensing surface temperature- and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration

Imperfect land physics introduce significant levels of uncertainty into current land surface models (LSMs) and can cause bias in their representation of land-atmosphere coupling strength (ρ). When LSMs are coupled with atmospheric prediction models, such errors will eventually degrade the accuracy of lower atmosphere forecasts. Here, we investigate the potential of two remote sensing (RS)-based ρ references for addressing LSM ρ bias. To minimize meteorological uncertainty and maximally attribute LSM ρ bias to land sources, we focus specifically on off-line LSM calibration forced using high-quality, observation-based meteorological data. Both ρ references are based on a newly proposed two-system approach for eliminating the impact of random errors in RS retrievals and quantified using the temporal correlations of soil moisture (SM) versus both evapotranspiration (ET) and the diurnal amplitude of surface temperature (dT). Experiments are conducted to calibrate an off-line LSM individually against each resulting ρ reference and using a combination of both dT- and ET-represented ρ references. The resulting calibrated LSM is further evaluated using independent ground-based ET observations and RS dT retrievals. Results show that although dT- and ET-represented ρ references are physically consistent across space, model calibration results based on them are quite different. Specifically, the calibration experiment targeting ET-represented ρ outperforms that targeting dT-represented ρ in ET and dT modeling. Diagnostic results indicate that the failure of dT-based calibration experiments is due to the confounding impacts of transpiration/evapotranspiration partitioning error and large dT uncertainties in LSM. However, results also confirm the potential of both dT- and ET-represented ρ references for jointly diagnosing and understanding LSM ρ bias. As a result, we suggest diagnosing LSM ρ bias using both ET- and dT-represented ρ references – but calibrating LSM using only ET-represented ρ reference data.

Hydrothermal Factors Influence on Spatial-Temporal Variation of Evapotranspiration-Precipitation Coupling over Climate Transition Zone of North China

As a land–atmosphere coupling “hot spot”, the northern China climate transition zone has a sharp spatial gradient of hydrothermal conditions, which plays an essential role in shaping the spatial and temporal pattern of evapotranspiration-precipitation coupling, but whose mechanisms still remain unclear. This study analyzes the spatial and temporal variation in land–atmosphere coupling strength (CS) in the climate transitional zone of northern China and its relationship with soil moisture and air temperature. Results show that CS gradually transitions from strong positive in the northwest to negative in the southeast and northeast corners. The spatial distribution of CS is closely related to climatic hydrothermal conditions, where soil moisture plays a more dominant role: CS increases first, and then decreases with increasing soil moisture, with the threshold of soil moisture at 0.2; CS gradually transitions from positive to negative at soil moisture between 0.25 and 0.35; CS shows an exponential decreasing trend with increasing temperature. In terms of temporal variation, CS is strongest in spring and weakens sequentially in summer, autumn, and winter, and has significant interdecadal fluctuations. The trend in CS shifts gradually from significantly negative in the west to a non-significant positive in the east. Soil moisture variability dominates the intra-annual variability of CS in the study regions, and determines the interannual variation of CS in arid and semi-arid areas. Moreover, the main reason for the positive and negative spatial differences in CS in the study area is the different driving regime of evapotranspiration (ET). ET is energy-limited in the southern part of the study area, leading to a positive correlation between ET and lifting condensation level (LCL), while in most of the northern part, ET is water-limited and is negatively correlated with LCL; LCL has a negative correlation with P across the study area, thus leading to a negative ET-P coupling in the south and a positive coupling in the north.

Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe

Abstract. The quantification of land–atmosphere coupling strength is still challenging, particularly in the atmospheric segment of the local coupling process chain. This is in part caused by a lack of spatially comprehensive observations of atmospheric temperature and specific humidity which form the verification basis for the common process-based coupling metrics. In this study, we aim at investigating where uncertainty in the atmospheric temperature and moisture affects the land–atmosphere coupling strength over Europe, and how changes in the mean temperature and moisture, as well as their vertical gradients, influence the coupling. For this purpose, we implemented systematic a posteriori modifications to the temperature and moisture fields from a regional climate simulation to create a spread in the atmospheric conditions. Afterwards, the process-based coupling metric convective triggering potential – low-level humidity index framework was applied to each modification case. Comparing all modification cases to the unmodified control case revealed that a strong coupling hotspot region in northeastern Europe was insensitive to temperature and moisture changes, although the number of potential coupling days varied by up to 20 d per summer season. The predominance of positive feedbacks remained unchanged in the northern part of the hotspot, and none of the modifications changed the frequent inhibition of feedbacks due to dry conditions in the atmosphere over the Mediterranean and the Iberian Peninsula. However, in the southern hotspot region in the north of the Black Sea, the dominant coupling class frequently switched between wet soil advantage and transition zone. Thus, both the coupling strength and the predominant sign of feedbacks were sensitive to changes in temperature and moisture in this region. This implies not only uncertainty in the quantification of land–atmosphere coupling strength but also the potential that climate-change-induced temperature and moisture changes considerably impact the climate there, because they also change the predominant atmospheric response to land surface wetness.

Effects of the surface coupling strength in the WRF/Noah-MP model on regional climate simulations over China

Land–atmosphere energy and moisture exchange can strongly influence local and regional climates. However, high uncertainty exists in the representation of land–atmosphere interactions in numerical models. Parameterization of surface exchange processes is greatly affected by parameter Czil, which, however, is typically defined as a domain-wide constant value. In this study, we examine the sensitivity of regional climate simulations over China to different surface exchange strengths via three Czil schemes (default (without Czil), constant (Czil = 0.1), and dynamic canopy-height-dependent Czil-h schemes) within a 13-km-resolution Weather Research and Forecasting model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP). Our results demonstrate that compared to the other two schemes, the Czil-h scheme substantially reduces land–atmosphere coupling strength overestimation, and comparison to Chinese terrestrial ecosystem flux research network (ChinaFLUX) observations reveals the capability of the Czil-h scheme to better match observed surface energy and water variations. The results of the application of the various Czil schemes in four typical climate zones in China demonstrate that the Czil-h simulations achieve the closest agreement with field observations. The Czil-h scheme can narrow the positive discrepancies in the simulated precipitation and surface fluxes and the negative biases of the land surface temperature in Northeast China, North China, eastern Northwest China, and Southwest China. In particular, the above remarkable improvements produced by the Czil-h scheme primarily occur in areas covered with short vegetation. Additionally, the precipitation simulated with the Czil-h scheme exhibits more intricate and uncertain changes compared with surface flux simulations due to the nonlocal impacts of the surface exchange strength resulting from atmospheric fluidity. Overall, our findings highlight the applicability of the dynamic Czil scheme as a better physical alternative to the current treatment of surface exchange processes in atmosphere coupling models.

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey's test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

AbstractThe parameterization of surface exchange coefficients (Ch) representing land–atmosphere coupling strength plays a key role in land surface modelling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is largely overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the impact of dynamic canopy‐height‐dependent coupling strength in an offline Noah LSM with multi‐parameterization options (Noah‐MP) when applied to China. Comparison with the default Noah‐MP LSM showed that the dynamic scheme significantly improved the Ch calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought about by the dynamic scheme differed across land‐cover types. The scheme was superior in reproducing the observed Ch as well as surface energy and water variables for short vegetation (grass, crops, and shrubs), whereas the improvement for tall canopy vegetation (forest) was not significant although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Further, the scheme can better reproduce the observed surface thermal components, while the improvement in hydraulic variables (such as soil moisture) remains limited. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has the potential to improve the representation of land–atmosphere interactions and, thus, the further development of LSM.

Impact of Deforestation on Land–Atmosphere Coupling Strength and Climate in Southeast Asia

Southeast Asia (SEA) is a deforestation hotspot. A thorough understanding of the accompanying biogeophysical consequences is crucial for sustainable future development of the region’s ecosystem functions and society. In this study, data from ERA-Interim driven simulations conducted with the state-of-the-art regional climate model COSMO-CLM (CCLM; version 4.8.17) at 14 km horizontal resolution are analyzed over SEA for the period from 1990 to 2004, and during El Niño–Southern Oscillation (ENSO) events for November to March. A simulation with large-scale deforested land cover is compared to a simulation with no land cover change. In order to attribute the differences due to deforestation to feedback mechanisms, the coupling strength concept is applied based on Pearson correlation coefficients. The correlations were calculated based on 10-day means between the latent heat flux and maximum temperature, the latent and sensible heat flux, and the latent heat flux and planetary boundary layer height. The results show that the coupling strength between land and atmosphere increased for all correlations due to deforestation. This implies a strong impact of the land on the atmosphere after deforestation. Differences in environmental conditions due to deforestation are most effective during La Niña years. The strength of La Nina events on the region is reduced as the impact of deforestation on the atmosphere with drier and warmer conditions superimpose this effect. The correlation strength also intensified and shifted towards stronger coupling during El Niño events for both Control and Grass simulations. However, El Niño years have the potential to become even warmer and drier than during usual conditions without deforestation. This could favor an increase in the formation of tropical cyclones. Whether deforestation will lead to a permanent transition to agricultural production increases in this region cannot be concluded. Rather, the impact of deforestation will be an additional threat besides global warming in the next decades due to the increase in the occurrence of multiple extreme events. This may change the type and severity of upcoming impacts and the vulnerability and sustainability of our society.

A sensitivity study on the response of convection initiation to in situ soil moisture in the central United States

Soil moisture can affect precipitation, however there is significant debate about the sign and strength of land–atmosphere coupling. This may be due to the differences regarding the selection of data sources, the presence of atmospheric persistence, and the spatial and temporal scales of the analysis. This study is a sensitivity analysis that quantifies how each of these factors influences land–atmosphere coupling in the central United States from 2005 to 2007. Four different sources of soil moisture are used to represent soil moisture: in situ measurements and three satellite products. The Thunderstorm Observation by Radar (ThOR) algorithm is used to identify convective events. The results show that warm-season afternoon convection initiates preferentially over dry soil, if in situ measurements are used. However, when satellite data are used the sign and strength of the soil moisture-precipitation coupling varies depending on which satellite dataset is used. This demonstrates that evaluations of land–atmosphere coupling strength are sensitive to the source of soil moisture. Our results also show that accounting for atmospheric persistence tends to weaken apparent land–atmosphere coupling. Changing the spatial scale of the analysis can also influence the sign and coupling strength. Analyses that are performed at a coarser spatial resolution tend to indicate a wet preference because they do not capture the local negative feedbacks. This study demonstrates that contradictions in the literature regarding the sign and strength of soil moisture-precipitation feedbacks can be partly attributed to differences in datasets, methods and scale of the analysis.

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Abstract Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET

Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and between H and P during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to P peaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as P/ETp approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather.

Radiation and energy balance dynamics over a rapidly receding glacier in the central Himalaya

Driven by the strength in local land–atmosphere coupling, inter‐annual variability of larger‐scale atmospheric circulations primarily determines a glacier's response to warming in high Asia. In this study, micrometeorological measurements in conjunction with regional reanalysis data set were analysed to examine seasonal land–atmosphere coupling strength at a typical central Himalayan (CH) glacier where the influence of Indian summer monsoon (ISM) predominates relative to winter‐westerlies. Energy–water (E–W) exchange and coupling behaviour were studied for the Pindari glacier based on sub‐hourly measurements of radiative–convective flux, state parameters, and sub‐surface thermal profiles using cross‐correlations between various E–W balance components. Coupling was positive in summer and winter accumulation seasons. However, it remained strongest during ISM. Coupling reversed during seasonal transition phases concurrent with distinct seasonality in E–W components. Lead–lag relation between some variables showed strong association at diurnal‐scale (VPD–Rn; VPD–LE; Rn–G), whereas some persisted beyond months (Rn–LE; Bowen ratio–precipitation; surface–air temperature). Weak association of variation of latent heat flux (LE) and rainfall was found during ISM at local scale than at regional scale, but with a lag, which was more prominent at regional scale. These observations indicate a seasonally variable coupling between E–W balance components through response–feedback mechanisms. Cross‐correlations of daily mean values of energy fluxes and meteorological variables reveal that Rn and air temperature are the prime drivers of energy balance. Net radiative energy (Rn) dominates energy exchanges at the glacier–atmosphere interface (governed primarily by the variation in net shortwave radiation), contributed on average 62% of the melt energy. However, sub‐surface heat flux along with the turbulent fluxes was the energy sinks of 24 and 15%, respectively. This study would help understand and parameterize E–W exchange pathways for ISM dominated CH glaciers in coupled glacier–climate models.

Impact of Land Surface Initialization and Land-Atmosphere Coupling on the Prediction of the Indian Summer Monsoon with the CFSv2

The impact of initial land-surface states on monthly to seasonal prediction skill of the Indian summer monsoon (June- September) is investigated using a suite of hindcasts made with the Climate Forecast System version 2 (CFSv2) operational forecast model. The modern paradigm of land-atmosphere coupling is applied to quantify biases in different components of the land-atmosphere coupled system and their effect on systematic errors. Three sets of hindcasts are performed for the period spanning 1982-2009 initialized at the start of April, May and June. For a particular initial date of a given year, one member (Control run) has the analyzed land initial state consistent with the atmosphere, sea ice and ocean states for that year; the other 27 members have land states taken from each of the remaining 27 years. There is significant improvement in the deterministic prediction skill of near surface temperature and soil moisture on monthly and seasonal time scales due to realistic land initial conditions. The improvement occurs in those areas where the land-atmosphere coupling is strongest. Improvements in the prediction skill of precipitation are confined to relatively small areas. The pattern of skill differences resembles patterns of land-atmosphere coupling strength, while biases in the representation of land-atmosphere coupling affect the skill of temperature and rainfall. The re-emergence of skill in temperature and precipitation towards the end of the season over northwest India within April and June IC hindcasts may be attributed to better simulation of the withdrawal phase of the monsoon as well as increased land-atmosphere coupling. For May IC hindcasts, increased skill in air temperature on the sub-seasonal time scales could be due to other large-scale factors. Errors in the parameterization of radiation, convection, boundary layer processes, surface moisture fluxes and the representation of vegetation contribute to decay in potential predictability and skill attributable to land initial conditions. Furthermore, incorrect representation of daily and sub-daily precipitation statistics over land also likely lead to errors in land-atmosphere coupling. Above all, the importance of accurate land surface initialization and land-atmosphere coupling in improving the Indian summer monsoon prediction on sub-seasonal to seasonal time scales is emphasized.

Impact of Arctic Wetlands on the Climate System: Model Sensitivity Simulations with the MIROC5 AGCM and a Snow-Fed Wetland Scheme

Abstract Wetlands cover large areas of the middle and high latitudes and influence the surface water and energy budget, surface hydrology, and the climate system. In this study, a scheme implicitly representing a snow-fed wetland, in which snowmelt can be stored with consideration of subgrid terrain complexity, was implemented in the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. An atmospheric general circulation model (AGCM) experiment was conducted using the Model for Interdisciplinary Research on Climate, version 5 (MIROC5), with and without the wetland scheme, with the main aim of reducing the model bias of warm and dry boreal summer at mid- to high latitudes. The experiment showed not only a better surface hydrology but also a weaker land–atmosphere coupling strength and larger (smaller) latent (sensible) heat flux due to the delayed snowmelt runoff. The summer warm and dry bias was partially improved over snowy and flat areas, particularly over much of western Eurasia and North America, without an apparent deterioration of simulated surface hydrology and climate over the rest of the land in the other seasons; the mean absolute error of 2-m air temperature and precipitation over land at 45°–90°N in summer decreased by 19% and 4%, respectively. The next step of model development will involve implementing an explicit representation of subgrid-scale surface water and related processes.

Sensitivities of Land Cover–Precipitation Feedback to Convective Triggering

Abstract The land surface state can be an important factor in the triggering of precipitation, whose depiction in Earth system models (ESMs) crucially relies on the representation of convective initiation. However, the sensitivity of land-cover change–precipitation feedbacks to different parameterized triggering criteria in ESMs has not been examined. In this study, a new triggering mechanism based on the heated condensation framework (HCF) is implemented in the Community Earth System Model (CESM). A set of land-cover change experiments with different convective triggering conditions are performed to evaluate the influence of convective triggering on land–atmosphere coupling strength and the response of summer afternoon precipitation to land-cover change over North America. Compared with the default parameterization, which depends on a CAPE threshold, the HCF trigger shows an improvement in the diurnal timing of summer precipitation but larger dry biases over much of the study area. With the HCF trigger, CESM exhibits weakened coupling strength between soil moisture and surface turbulent fluxes over the Great Plains. The surface temperature deficit, as an additional triggering criterion in HCF, is not significantly coupled with surface fluxes over the central Great Plains despite strong latent heat–CAPE coupling. In contrast to the CAPE-trigger simulations, which indicate increased precipitation over the Great Plains after agricultural expansion, the HCF-trigger simulations show significantly increased afternoon precipitation only over the northern plains, which is mainly associated with more frequent deep convection. The discrepancies suggest caveats when investigating the impacts of land-cover change on precipitation, because the magnitude and spatial patterns of precipitation change can be greatly affected by the treatment of convection in ESMs.

Analyzing energy–water exchange dynamics in the Thar desert

Regions of strong land–atmosphere coupling will be more susceptible to the hydrological impacts in the intensifying hydrological cycle. In this study, micrometeorological experiments were performed to examine the land–atmosphere coupling strength over a heat low region (Thar desert, NW India), known to influence the Indian summer monsoon (ISM). Within the vortex of Thar desert heat low, energy–water exchange and coupling behavior were studied for 4 consecutive years (2011–2014) based on sub-hourly measurements of radiative–convective flux, state parameters and sub-surface thermal profiles using lead-lag analysis between various E–W balance components. Results indicated a strong (0.11–0.35) but variable monsoon season (July–September) land–atmosphere coupling events. Coupling strength declined with time, becomes negative beyond 10-day lag. Evapotranspiration (LE) influences rainfall at the monthly time-scale (20–40 days). Highly correlated monthly rainfall and LE anomalies (r = 0.55, P < 0.001) suggested a large precipitation memory linked to the local land surface state. Sensible heating (SH) during March and April are more strongly (r = 0.6–0.7) correlated to ISM rainfall than heating during May or June (r = 0.16–0.36). Analyses show strong and weak couplings among net radiation (Rn)–vapour pressure deficit (VPD), LE–VPD and Rn–LE switching between energy-limited to water-limited conditions. Consistently, +ve and −ve residual energy [(dE) = (Rn − G) − (SH + LE)] were associated with regional wet and dry spells respectively with a lead of 10–40 days. Dew deposition (18.8–37.9 mm) was found an important component in the annual surface water balance. Strong association of variation of LE and rainfall was found during monsoon at local-scale and with regional-scale LE (MERRA 2D) but with a lag which was more prominent at local-scale than at regional-scale. Higher pre-monsoon LE at local-scale as compared to low and monotonous variation in regional-scale LE led to hypothesize that excess energy and water vapour brought through advection caused by pre-monsoon rainfall might have been recycled through rainfall to compensate for early part of monsoon rainfall at local-scale. However, long-term measurements and isotope analysis would be able to strengthen this hypothesis. This study would fill the key gaps in the global flux studies and improve understanding on local E–W exchange pathways, responses and feedbacks.

Irrigation Effects on Land–Atmosphere Coupling Strength in the United States

Abstract Land–atmosphere coupling strength describes the degree to which the atmosphere responds (e.g., via changes in precipitation) to changes in the land surface state (e.g., soil moisture). The Midwest and Great Plains of the United States have been shown to be “hot spots” of coupling by many climate models and some observations. However, very few of the modeling studies have reported whether the climate models applied irrigation in the Midwest and Great Plains, where 24%–27% of farmland is irrigated, leaving open the question of whether irrigation affects current estimates of coupling strength. This study used a regional climate model that incorporated dynamic crop growth and precision irrigation (WRF3.3–CLM4crop) to investigate irrigation effects on land–atmosphere coupling strength. Coupling strength was quantified using multiple indices and the irrigated land-induced precipitation was tracked using a back trajectory method. The indices showed a consistent and significant decline in local coupling strength with irrigation in the Midwest and northern Great Plains. These reductions were due to increased soil moisture but decreased local precipitation and lower sensitivity of latent heat flux to soil moisture over irrigated regions. The back trajectories of water vapor transport confirmed that irrigation largely did not contribute to local precipitation. Water vapor from irrigated land was transported to the Midwest and U.S. Northeast where it fell as precipitation, suggesting that irrigation has a broader spatial impact on soil moisture–precipitation coupling than simply through local soil moisture–evapotranspiration coupling. The present study suggests that climate models without irrigation schemes may overestimate the land–atmosphere coupling strength over irrigated agricultural regions but underestimate coupling strength over neighboring nonirrigated regions.

Diagnosing an Artificial Trend in NLDAS-2 Afternoon Precipitation

Abstract While investigating linkages between afternoon peak rainfall amount and land–atmosphere coupling strength, a statistically significant trend in phase 2 of the North American Land Data Assimilation System (NLDAS-2) warm season (April–September) afternoon (1700–2259 UTC) precipitation was noted for a large fraction of the conterminous United States, namely, two-thirds of the area east of the Mississippi River, during the period from 1979 to 2015. To verify and better characterize this trend, a thorough statistical analysis is undertaken. The analysis focuses on three aspects of precipitation: amount, frequency, and intensity at 6-hourly time scale and for each calendar month separately. At the NLDAS-2 native resolution of 0.125° × 0.125°, Kendall’s tau and Sen’s slope estimators are used to detect and estimate trends and the Pettitt test is used to detect breakpoints. Parallel analyses are conducted on both NARR and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), subdaily precipitation estimates. Widespread breakpoints of field significance at the α = 0.05 level are detected in the NLDAS-2 frequency and intensity series for all months and 6-h periods that are absent from the analogous NARR and MERRA-2 datasets. These breakpoints are shown to correspond with a July 1996 NLDAS-2 transition away from hourly 2° × 2.5° NOAA/CPC precipitation estimates to hourly 4-km stage II Doppler radar precipitation estimates in the temporal disaggregation of CPC daily gauge analyses. While NLDAS-2 may provide the most realistic diurnal precipitation cycle overall, users should be aware of this discontinuity and its direct effect on long-term trends in subdaily precipitation and indirect effects on trends in modeled soil moisture, surface temperature, surface energy and water fluxes, snow cover, snow water equivalent, and runoff/streamflow.