Land Surface Processes Research Articles

A prognostic vegetation phenology model to predict seasonal maximum and time series of global leaf area index using climate variables

Vegetation phenology models provide us with tools for understanding and predicting vegetation responses to a changing climate. Vegetation phenology can be characterized by periodic changes in leaf area index (LAI), which quantifies leaf areas per unit ground surface areas. As an essential variable in land surface studies, LAI is generally used to simulate a range of fundamental vegetation processes and is essential to the simulations of land surface processes and land-atmosphere fluxes. Yet few studies directly provide one key variable required in simulating LAI, the annual seasonal maximum leaf area index (LAISM). There is a need to develop a method for predicting LAISM and allow for simulating daily LAI time series (LAITS). In this study, we developed a Prognostic Vegetation Phenology Model (PVPM) that predicts both LAISM and daily LAITS across biomes at the site and global scales, using climate variables. The method predicts LAISM based on an improved Budyko-like function and then predicts LAITS by integrating a previously developed semi-prognostic model. Experimental results show that PVPM is suitable for simulating vegetation dynamics and provides two parameterization schemes across different biomes at both site and global levels. For different biomes, the model could explain a large variance in satellite-derived LAISM and daily LAITS at the site scale, and well capture the spatial patterns and temporal trends of satellite-derived LAISM and LAITS across the globe. Compared to results obtained from other two process-based climate models (i.e., the GFDL-CM4 and CESM2), PVPM could make predictions on LAITS that match global satellite observations well (r = 0.9 and RMSE < 0.82). This study provides a prognostic vegetation phenology model that can be used in the land surface models to forecast global LAI time series for modeling vegetation processes.

A novel laboratory method for the retrieval of the soil water retention curve from shortwave infrared reflectance

The soil water retention curve (SWRC) is an essential soil property that relates soil water content and matric potential. It plays a crucial role in soil water dynamics and the understanding of various hydrological phenomena at the land surface, including infiltration, runoff, evaporation, and energy exchange processes. In recent years, proximal sensing methods have shown great potential for retrieving this challenging-to-measure property from spectral reflectance. However, a physically-based approach is still lacking as current methods rely on empirical data-driven algorithms. Here we propose a novel physics-based laboratory method that, for the first time, enables direct estimation of the entire SWRC from saturated to dry using soil water content/reflectance data pairs within the shortwave infrared domain. The main hypothesis underlying the new method is that soil optical properties not only vary with soil water content but also with the pore scale distribution of capillary and adsorbed soil water. For evaluation, retrieved soil water retention curves of 21 soils that vastly differ in physical and hydraulic properties were compared to direct measurements. The results suggest that the new method is a rapid and efficient alternative to established laboratory measurement methods.

Sensitivity Analysis of the Land Surface Characteristic Parameters in Different Climatic Regions of the Loess Plateau

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

Revisiting the timing of Saginaw lobe ice retreat and implications for drainage adjustments across southern Michigan, USA

Understanding the behavior of paleo-ice sheets in response to past climate change and land surface processes can provide insights into the interplay between the atmosphere, ocean surface, and the cryosphere, which helps update current climate models and understand landscape evolution. The timing of the advancement and retreat of ice lobes along Quaternary ice sheet margins and their link to ocean surface temperature fluctuation are of particular interest. The Saginaw lobe along the southern margin of the Laurentide Ice Sheet (LIS) was bound by the Lake Michigan and Huron-Erie lobes. It flowed out of Saginaw Bay, across Lower Michigan, and into northern Indiana. All three of these lobes retreated across the region asynchronously, with the Saginaw lobe retreating first. The asynchronous behavior of the lobes allowed the Lake Michigan and Huron-Erie lobes to overprint and alter landscapes once occupied by Saginaw lobe ice.Lacustrine sediments within the Sturgis Moraine, deposited during the Saginaw lobe retreat in extreme southwestern Michigan, yielded optically stimulated luminescence (OSL) ages of 30.1 ± 1.8, 28.7 ± 1.5 and 25.2 ± 1.9 ka. An additional radiocarbon date (22.22 ± 0.8 cal ka) was measured from bryophytes sampled near the base of an ice-walled lake plain atop the moraine. These dates indicate that deglaciation of the Saginaw lobe could have been underway in southwestern Michigan between ∼ 30 and 22 ka, while ice margins of the Huron-Erie and Lake Michigan lobes were further south in Indiana and Illinois. The new dates indicate that southwestern Michigan may have witnessed ice-free conditions four thousand years earlier than previously thought. Moreover, the paleo-hydrological analyses of southwestern Michigan using detailed surficial geology mapping, Lidar-based longitudinal and terrace profiles along meltwater channels, and OSL data indicate that meltwater originating from the Saginaw—Huron-Erie interlobate area drained westward across the Saginaw lobe terrain between ∼ 18.0 to 14.6 Ka ago. The findings call for revisiting the current understanding of the timing and behavior of the southern margins of the LIS and the Holocene landscape evolution in Michigan.

Performance of regional climate model RegCM4 with a hydrostatic or non‐hydrostatic dynamic core at simulating precipitation extremes in China

AbstractIn this study, we investigated the performance of RegCM4 with a hydrostatic or non‐hydrostatic dynamic core and different land surface models at simulating the mean and extreme precipitation during 1991–1999 in China and the Beijing–Tianjin–Hebei urban agglomeration (JJJ) in northern China. The resolutions were 12 km with the whole of China included and 3 km nested over the JJJ region. The observed daily precipitation data comprised the CN05.1 data set from China Meteorological Administration. First, RegCM4 with a hydrostatic dynamic core forced by the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR) that participated in CMIP6 was used to simulate the east Asia area at a resolution of 12 km. Next, the RegCM4 results with 12 km resolution forced RegCM4 with a non‐hydrostatic dynamic core (RegCM4‐NH) to run over the JJJ region with a 3 km convection‐permitting scale through one‐way nesting. The results showed that RegCM4 with a hydrostatic and non‐hydrostatic core could obtain more detailed seasonal and annual precipitation in China and the JJJ region relative to MPI‐ESM1.2‐HR although their performance varied over different regions. RegCM4 with the community land surface model 4.5 (CLM4.5) generally performed better at simulating the mean and extreme precipitation over the four subregions of China than that with the Biosphere–Atmosphere Transfer Scheme because of more detailed descriptions of land surface processes of CLM4.5. RegCM4‐NH with convection‐permitting resolution improved the simulations of heavy precipitation extremes over JJJ compared with RegCM4 with a hydrostatic core and resolution of 12 km. In addition, RegCM4‐NH with CLM4.5 performed better at simulating heavy precipitation extremes over JJJ compared with all other simulations, with higher correlation coefficients and larger relative root mean squared errors.

A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

How Land Surface Characteristics Influence the Development of Flash Drought through the Drivers of Soil Moisture and Vapor Pressure Deficit

Abstract As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the United States that are crucial for agriculture, forestry, and natural ecology. A pressing need is to understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. To better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the United States. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the standardized evaporative stress ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture coevolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the United States. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events.

Soil Moisture Estimation by Assimilating In‐Situ and SMAP Surface Soil Moisture Using Unscented Weighted Ensemble Kalman Filter

AbstractHighly accurate soil moisture information is necessary to understand land surface processes. However, observational techniques do not produce adequately accurate spatial‐temporal continuous regional soil moisture data. The data assimilation method can be used to improve the soil moisture estimations by merging multi‐source observed data, but its performance is affected by error covariance and the quality of assimilated data. We designed eight numerical experiments to analyze how to improve the filter performance through soil moisture assimilation using the unscented weighted ensemble Kalman filter (UWEnKF) and 1‐D Richards equation at Maqu and Erlinghu (ELH) observational sites in the source region of Yellow River (SRYR), China. The experimental results show that the filter performance improves as the quality of assimilated data increases in the soil moisture assimilation experiment when assimilating in‐situ surface soil moisture (SSM) observations, SMAP SSM data and downscaled SMAP SSM data. In other aspects, filter performance is readily affected by model and observation error covariances in soil moisture assimilation. If the SMAP SSM data are taken to be perfect (i.e., small bias), UWEnKF performs differently between different sites because of the underestimation or overestimation of SMAP SSM and model simulations compared to the in‐situ observations. Additionally, different soil moisture assimilation results can be obtained with different initial values at the beginning of the assimilation period. Overall, filter performance can be improved primarily by improving the quality of assimilated data (e.g., downscaling the remote sensing data), and by creating a reasonable and effective method for determining error covariance.

First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding

Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.

Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)

Abstract. The layout of urban buildings shows significant heterogeneity, which leads to the significant spatial inhomogeneity of the wind field in and over the canopy of urban street canyons. However, most of the current urban canopy models do not fully consider the heterogeneity of the urban canopy. Large discrepancies thus exist between the wind speeds simulated by the current urban canopy models and those observed in the street canyon. In this study, a parameterization scheme for wind fields, Inhomogeneous Wind Scheme for Urban Street (IWSUS), is developed to better characterize the heterogeneity of the urban canopy. We use a computational fluid dynamics method to generate the IWSUS scheme and compare it with observations of the wind profile and turbulent flux in and over the street canyon for validation. In IWSUS, the wind speed vertical profiles at six representative positions located in a typical street canyon (i.e., the windward or leeward side of a long straight street or the inflow or outflow end) are parameterized separately. The wind profile by IWSUS thus can better describe the horizontal heterogeneity of the urban near-surface wind field, e.g., the dynamic drag effect of buildings in the lower atmospheric layer over the urbanized land use. The validation based on observations shows that the performance of simulation results by IWSUS is better than that by the exponential–logarithmic (exp-log) law widely used in the current urban schemes. We consider typical building arrangement and specific street orientations in IWSUS for wind field simulations, which can better match the distribution characteristics of street canyons around the observation point in the street canyon. The averaged wind profiles and turbulence energy fluxes in the model grids of urban areas by IWSUS are also nearer to the observations than those by the exp-log law. The normalized mean errors (NMEs) between the simulated and the observed vertical average wind speed are 49.0 % for IWSUS and 56.1 % for exp-log law in the range from the ground to 4 times the average height of the buildings and 70 % for IWSUS and 285.8 % for exp-log law in the street canyon (range from the ground to building top). This study proves that the accuracy of simulations of land surface processes and near-ground meteorological processes over the urban canopy can be improved by fully considering the heterogeneity of the urban canopy layout structures and the inhomogeneity of wind field distributions in and over the street canyon. IWSUS is expected to be coupled with mesoscale atmospheric models to improve the accuracy of the wind field, land surface energy budget, meteorological and atmospheric chemistry simulations.

A drier than expected future, supported by near-surface relative humidity observations.

Despite continuous progress in climate modeling, global projections of the terrestrial water cycle remain highly model dependent. Here, we use quality-controlled gridded observations of temperature and humidity to constrain projected changes in continental near-surface relative humidity across the 21st century. Results show that the projections are poorly constrained when using surface temperature observations only and argue for mitigation policies that are not only rooted in global warming levels. Projections constrained with both near-surface temperature and relative humidity observations show an inevitable continental drying, especially in the northern midlatitudes where anthropogenic aerosols have, however, countered this long-term response until the late 1980s. A "strong drying" storyline is then used to highlight the urgent need for careful adaptation strategies and to suggest a possible contribution of land surface processes to model uncertainties.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Impacts of seasonally frozen soil hydrothermal dynamics on the watershed hydrological processes inferred from a spatially distributed numerical modelling approach

The freeze–thaw cycle over the surface seasonally frozen soil is an important soil hydrothermal dynamic process linking land surface processes and climatic changes in the cold regions. With the advancement of frozen soil hydrothermal dynamic studies and remote sensing technology, the simulation of frozen soil hydrological processes based on the distributed numerical model has become a hotspot to better understand the impact of frozen soil hydrothermal dynamics on the watershed hydrological processes in the cold regions over a large spatial scale. However, the quantitative analysis of the impact of seasonally frozen soil hydrothermal processes on watershed runoff at long-term time scales remained an unsolved issue in the field of frozen soil hydrology. Under the framework of the watershed distributed eco-hydrological model ESSI-3, a fully distributed frozen soil hydro-thermal processes integrated modeling system (FFIMS model) was established based on the coupled water and heat transferring mechanism for frozen soil hydro-thermal process simulations in the frozen surface or at a certain depth of a watershed. By coupling the FFIMS model with the distributed eco-hydrological model ESSI-3, the impacts of seasonally frozen soil hydrothermal processes on hydrological processes were investigated from the perspective of temporal-spatial domain with the simulated hydrothermal and hydrological processes for a long-term period from 2008 to 2016 over a watershed located in the cold region of Northeastern China. The results suggested that the soil freeze–thaw cycling posed different impacts with limited significance throughout the whole hydrological processes of the watershed in different seasons. Significant impacts on the hydrological processes were particularly observed in the thawing period of a year, when soil ice meltwater contributing to the discharge of the study watershed reached to about 35% in average in this period. ESSI-3 coupled with the FFIMS modelling system obviously improved the performance of the original ESSI-3 in cold region watershed simulations, and the averaged Nash efficiency coefficients obtained increased from almost 0 to 0.77 in the thawing period of a year. The study demonstrated the importance of application of spatially distributed numerical model with physical mechanism for seasonally frozen soil water and heat transfer process simulations.

Forest Phenology under Differing Topographic Conditions: A Case Study of Changbai Mountain in Northeast China

Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R2 = 0.93, p &lt; 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R2 = 0.90, p &lt; 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R2 = 0.53, p = 0.04), EOS was delayed by 0.18 d (R2 = 0.82, p = 0.002), and the LOS increased by 0.28 d (R2 = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

Role of Winter Soil Moisture in Subsequent Summer Thermal Anomalies on the Tibetan Plateau

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

Characteristics of Winter Precipitation over Pakistan and Possible Causes during 1981–2018

Winter (December to March) precipitation is the major source of rainfed agriculture, storage, and perennial water flow in the western river system of Pakistan. Hence, this study uses precipitation data and variables of land–ocean and atmosphere from the Pakistan Meteorological Department and European Centre for Medium-Range Weather Forecasts (ECMWF) and fifth-generation reanalysis data (ERA5), respectively, to investigate the changes in winter precipitation and its sensitivity to different land–ocean and atmosphere variables, which are rarely investigated in Pakistan. Non-parametric techniques, such as the modified Mann–Kendal, Sen slope, kernel density-based probability function (PDF), empirical orthogonal function (EOF), and correlation analysis, were used to assess the changes and modes of variability in winter precipitation. The overall seasonal precipitation showed a significant decreasing trend with a (−0.1 mm d−1 yr−1) in the seasonal mean and monthly precipitation, except in February which showed a significant increase (&gt;0.11 mm d−1 yr−1). The highest decrease in daily precipitation (&lt;−0.1 mm d−1 yr−1) was in the north, with a moderate decrease in the southeast. The extreme precipitation indices exhibited an erratic decreasing tendency, but the maximum daily precipitation index increased; post-2000 precipitation extremes displayed an increase, and the seasonal and monthly precipitation exhibited the highest deviations during the drought period (1995–2000). The leading precipitation mode (EOF1) is sensitive to the local land surface processes and significantly correlated (&gt;0.60) with the central Pacific and Indian Ocean’s basin-wide sea surface temperature, corroborating the influence of ENSO-induced meridional/zonal deviation of Hadley–Walker circulations. The Hadley and Walker cells affect the south-westerlies’ jet stream strength, impacting the water vapor transport and precipitation over Pakistan. These changes in the precipitation magnitude will affect rain-fed agriculture, especially the Rabi cropping pattern and perennial river flow.

Climate feedbacks associated with land-use and land-cover change on hydrological extremes over the Yangtze River Delta Region, China

Quantifying how land-use and land-cover change (LULCC) drives extreme events is a priority to inform adaptation planning in a changing environment. Previous studies mainly focus on the effects of local LULCC on runoff generation processes; however, few on the effects of the climate feedbacks of LULCC. Here we couple the Variable Infiltration Capacity model with climate forcing from the Land Use Model Intercomparison Project, part of the new state-of-the-art Coupled Model Intercomparison Project phase 6, to synthetically detect global LULCC-induced impacts on hydrological extremes in one of the fastest developing areas in China and the world, i.e., the Yangtze River Delta (YRD). Results suggest that global LULCC increased extreme rainfall by 1.41 ∼ 3.90 % with spatial heterogeneity over YRD, especially during the rapid urbanization period. Regions with increasing extreme rainfall during the rapid urbanization stage match well with the city belt distribution, indicating the precipitation enhancement effects of the urban land surface. The combined impacts (with both LULCC and its climate feedbacks) lead to a 3.01–7.18 % increase in extreme runoff, much higher than the effects of LULCC only (0.05–0.14 %). The consistent signs of changes in extreme rainfall and runoff indicate the dominant role of LULCC-induced climate feedback in the changes in extreme runoff over the YRD region. In addition, flash droughts increased when considering the climate feedbacks of global LULCC, particularly during the rapid urbanization stage. LULCC tends to increase average flows, high flows, and low flows, particularly in an urban-dominated basin. When considering the climate feedback effects from global LULCC, the LULCC would lead to a larger increase in high flows and average flows, thus floods. We highlight a need to consider land–atmosphere interactions in addition to land-surface processes in projecting changes in hydrological extremes.

Influence of Land–Atmosphere Coupling on Low Temperature Extremes Over Southern Eurasia

AbstractLand–atmosphere coupling is a crucial factor influencing extreme weather and climate. Based on the ERA5‐Land reanalysis data set and sensitivity experiments with the CESM1.2.2 model, this study investigated the possible impacts of land–atmosphere coupling on low temperature extremes (LTEs) during fall and winter over southern Eurasia (SEA). The results suggested that LTEs show evident interdecadal variations in recent 60 years (1958–2017) and exhibit significant positive correlations with soil moisture anomalies based on the ERA5‐Land data set. Numerical simulations (the control run and sensitivity run with prescribed climatological soil moisture) isolated the effects of soil moisture‐related land surface processes and demonstrated that land–atmosphere coupling explains around 70% of temperature variability, increases the near‐surface air temperature by 0.81 K, and favors the reduction of LTEs by 8.20 d·yr−1. In the perturbation scenarios, the impacts of coupling on the frequency and spatial coverage of LTEs are more prominent. Further analysis revealed that the thermal cooling anomalies induced by the coupling could alter the atmospheric circulation, thereby affecting the frequency and intensity of LTEs. When the land–atmosphere interactions are weakened, the surface latent heat flux increases accompanied by intense evapotranspiration, while the sensible heat flux and net infrared radiation decrease. Land surface cooling is apparent, and it weakens with increasing altitude, along with geopotential height anomalies that are positive in the lower troposphere and weaker at higher altitudes. The northeasterly wind anomalies over the East European Plain facilitate the development of cold advection, providing a favorable circulation background for the generation of LTEs over SEA.

Impact of alternative soil data sources on the uncertainties in simulated land-atmosphere interactions

Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications.