Annual Scale Research Articles

Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

ABSTRACT Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

Comparison of different rainfall products with gauge-based measurements over Narmada River Basin, India.

The present study evaluates and compares the performance of different rainfall products, namely, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), India Meteorological Department (IMD) gridded, Prediction of Worldwide Energy Resource (POWER), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR) with gauge-based measurements over Narmada River basin, India. The ground-based daily rainfall data (1981-2020) of 11 gauging stations have been collected from the Water Resources Department, Madhya Pradesh and the evaluation of rainfall product has been accomplished on a point-to-grid basis (nearest neighbor method) at annual and seasonal scales with the help of continuous and categorical statistical metrics. The results reveal a strong positive correlation (> 0.75) between rainfall estimates of different products and gauge-based measurements at annual scale demonstrating higher similarity in rainfall estimates and observed data, whereas seasonal estimates have exhibited comparatively weaker relationship. Likewise, percent bias (PBIAS) demonstrates least bias in annual and monsoon rainfall estimates and high in other seasons. These findings reveal that rainfall estimates tend to improve with increasing time scale (season to annual). However, majority of the rainfall products have overestimated the low rainfall (western region) and underestimated the high rainfall (eastern and southeastern regions). Further, the values of critical success index (CSI) indicate IMD gridded product outperforms in detecting rainfall events accurately followed by POWER, PERSIANN-CDR, and CHIRPS. These results suggest that IMD gridded estimates provide the best alternate to ground-based rain measurements. However, rainfall estimates from POWER, PERSIANN-CDR and CHIRPS can also be used in various hydrometeorological investigations over Narmada River basin.

Evaluating and enhancing the performance of the K-Means clustering algorithm for annual coastal bed evolution applications

The prediction of the coastal bed evolution at an annual scale utilizing process-based models is usually a complex task requiring significant computational resources. To compensate for this, accelerating techniques aiming at reducing the amount of input parameters are often employed. In the framework of this research, a comprehensive evaluation of the capacity of the widely-used K-Means clustering algorithm as a method to obtain representative wave conditions was undertaken. Various enhancements to the algorithm were examined in order to improve model results. The examined tests were implemented in the sandy coastline adjacent to the port of Rethymno, Greece, utilizing an annual dataset of wave characteristics using the model MIKE21 Coupled Model FM. Model performance evaluation was carried out for each test simulation by comparing results to a “brute force” one, containing the bed level changes induced from the annual time series of hourly changing offshore sea state wave characteristics, deeming the results very satisfactory. The best-performing configurations were found to be related to the implementation of a filtering methodology to eliminate low-energy sea states from the dataset. Employment of clustering algorithms utilizing “smart” configurations to improve their performance could become a valuable tool for engineers desiring to obtain an accurate representation of annual bed level evolution, while simultaneously reducing the required computational effort.

Ammoniated straw returning: A win-win strategy for increasing crop production and soil carbon sequestration

Increasing crop production and soil carbon sequestration is critical for sustainable agricultural development. Straw returning, a common agricultural practice, positively affects grain yield and soil organic carbon. Nevertheless, the slow decomposition rate and high carbon-to-nitrogen ratio of straw are significant obstacles to its effectiveness. Ammonifying straw using urea and calcium hydroxide may be a promising solution, whereas limited information is available on the impacts of ammoniated straw returning on crop production and soil carbon sequestration. Therefore, in a winter wheat-summer maize rotation system, we conducted an experiment with three straw returning treatments (T1, straw removal; T2, traditional straw returning; T3, ammoniated straw returning) to investigate their impacts on crop growth, grain yield and yield components, carbon inputs and outputs, and net ecosystem carbon budget (NECB). The results showed that T3 outperformed T2 regarding increasing leaf area index and aboveground biomass. Ear density and thousand kernel weight of winter wheat and hundred kernel weight of summer maize were ranked T1 < T2 < T3, with no significant differences between treatments for other grain yield components. Consequently, winter wheat, summer maize, and annual grain yields were the highest under T3, followed by T2 and T1. In comparison with T1, T2 and T3 increased annual total carbon inputs by 5463 and 7271 kg C ha–1, respectively, due to the increased carbon inputs from returned straw and net primary productivity. Annual carbon total outputs, primarily from aboveground biomass removal at harvest and carbon respiration, increased by 3305 kg C ha–1 under T2 and 4264 kg C ha–1 under T3, compared with T1. Balancing annual total carbon inputs and outputs, T1, T2, and T3 had annual NECB values of –1215, 944, and 1793 kg C ha–1, respectively. Therefore, the T1 agroecosystem acted as a carbon source, whereas the T2 and T3 agroecosystems, particularly T3, acted as a carbon sink on an annual scale. These results suggest that ammoniated straw returning may be a superior straw application practice for increasing crop production and soil carbon sequestration simultaneously.

Water cycle changes in Czechia: a multi-source water budget perspective

Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.

Hydrological Projections under CMIP5 and CMIP6: Sources and Magnitudes of Uncertainty

Abstract Projections of future hydrological conditions rely largely on global climate models, but model performance varies greatly. In this study, we investigated projected changes in runoff (R), precipitation (P), evapotranspiration (ET), and soil moisture (SM) based on the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) and quantified the uncertainties of their projected changes on annual and seasonal scales. The results indicate that all four hydrological variables show an increase over most of the global land: annual projections of R, P, ET, and SM from CMIP6 increase in 72%, 81%, 82%, and 66% of the global land area, respectively, under a high emissions scenario during the period 2080–99 relative to 1970–99. We estimated the uncertainties in CMIP6 from different sources on an annual scale and found that model uncertainty dominates the total projected uncertainties during the twenty-first century [76% (R), 73% (P), 89% (ET), and 95% (SM) in the 2090s], and the contribution of internal variability decreases with time. The low-latitude regions have the greatest uncertainty in hydrological projections. In CMIP6, the uncertainty of projected changes in P contributes the most to the uncertainty of projected changes in R, with a contribution of 93% on annual scale, followed by ET and SM. Overall, the performances of the CMIP5 and CMIP6 models are similar in terms of hydrological changes and the composition of their uncertainties. This study provides a theoretical reference for the further improvement and development of hydrological components in global climate models.

Amplified Production and Export of Dissolved Inorganic Carbon During Hot and Wet Subtropical Monsoon

AbstractUnderstanding the origins and processes of riverine dissolved inorganic carbon (DIC) is crucial for predicting the global carbon cycle with projected, more frequent climate extremes yet our knowledge has remained fragmented. Here we ask: How and how much do DIC production and export vary across space (shallow vs. deep, uphill vs. depression) and time (daily, seasonal, and annual)? How do the relative contributions of biogenic (soil respiration) and geogenic (carbonate weathering) sources differ under different temperature and hydrological conditions? We answer these questions using a catchment‐scale reactive transport model constrained by stream flow, stable water isotopes, stream DIC, and carbon isotope data from a headwater karstic catchment in southwest China in a subtropical monsoon climate. Results show climate seasonality regulates the timing of DIC production and export. In hot‐wet seasons, high temperature accelerates soil respiration and carbonate weathering (up to a factor of three) via elevating soil CO2 and carbonate solubility, whereas high discharge enhances export by two orders of magnitude compared to cold‐dry seasons. Carbonate weathering is driven more by soil CO2 than water flow. At the annual scale, 92.9% and 7.1% of DIC was produced in shallow and deep zone, respectively, whereas 64.5% and 35.5% of DIC was exported from shallow and deep zone, respectively. These results highlight the uniqueness of subtropical karst areas as synchronous reactors and transporters of DIC during the hot‐wet monsoon, contrasting the asynchronous production and export in other climate regions. A future hotter and wetter climate with more intensive storms in the region may further intensify DIC production and export, accentuating the potential of subtropical karst regions as global hot spots for carbon cycling.

Hydrometeorological variation in the middle and upper reaches of the Yellow River Basin (1960–2019)

The hydrological and meteorological elements of the Yellow River Basin have undergone fluctuations in response to both climate change and human activities. Understanding its evolutionary trends is essential for the development of effective water conservation measures. Employing linear fitting, Mann–Kendall, and wavelet analysis techniques, this study scrutinizes the trends and periodicity in precipitation, air temperature, and runoff within the middle and upper reaches of the Yellow River Basin from 1960 to 2019. The findings elucidate that temperatures in the middle and upper reaches of the Yellow River have been progressively increasing on an annual basis, while precipitation exhibits periodic fluctuations, and annual runoff demonstrates a declining trajectory. Sudden changes in runoff and temperature transpired in the years 1985 and 1997, respectively, whereas sudden changes in annual precipitation occurred in 1961 and 2012. On an annual scale, the influence of temperature and precipitation on runoff displays no evident prominent cycle. Notably, a significant negative correlation exists between temperature and runoff, with the noteworthy impact of temperature on runoff primarily manifesting in the 6-year sub-cycle spanning 1986 to 1992, as well as the 9-year sub-cycle encompassing 1963 to 1972. Conversely, precipitation and runoff exhibit a significant positive correlation. The substantial effect of precipitation on runoff is mainly concentrated within the 9-year sub-cycle from 1963 to 1972 and the 8-year sub-cycle from 1972 to 1980. Precipitation emerges as the primary driving force behind watershed runoff. In essence, the study's outcomes unveil the implications of climate change on runoff within the middle and upper reaches of the Yellow River Basin, thereby offering pertinent insights for effective water resource management strategies in the face of evolving climatic conditions.

Characteristics of annual and seasonal precipitation in North Macedonia: change analysis and correlation with the North Atlantic Oscillation (1951–2010)

Studying the spatiotemporal precipitation characteristics in North Macedonia (1951–2010) is important as no spatially concurrent precipitation changes across the Balkan Peninsula have been identified. North Macedonia lies at the intersection between Mediterranean and continental climate zones and an improved understanding might help to better understand the regional precipitation patterns. The analysis shows a spatially consistent, high inter-annual variability, which makes trend detection difficult. Statistically significant decreasing trends were only found in seasonal precipitation at three stations. Changes in all other precipitation series were non-significant. Trends in winter, spring, and at annual scale are generally decreasing, whereas in summer are increasing. To better understand possible mechanisms behind the observed variability and change, correlations with the North Atlantic Ocsillation (NAO) were assessed. Significant and regionally concurrent correlations were detected. A strong correlation of the previous winter NAO-index with spring precipitation was found, which is valuable information for anticipatory water resources management in the region.

Temporal variations of Rainfall over Konkan &amp; Goa during 1901-2020

This study examines the temporal variation of rainfall on monthly, seasonal, annual and decadal scale over Konkan &amp; Goa, India during 1901-2020. Trend analysis of rainfall data is carried out by using Man-Kendall and t-test. A significant increasing trend has been observed in annual rainfall data. A significant increasing trend of 32mm/year is present in annual rainfall. Southwest monsoon showed significant increasing rainfall trends over Konkan &amp; Goa during the last 120 years. On the monthly scale, rainfall indicate significant increasing trend during the month of June, August, September and October showed and significant decreasing trend during January &amp; February. During the period of 120 year rainfall is highest in period of 1931-1960. Decadal rainfall analysis shows total 18 excess years and 15 deficit years observed annually over the period of study.

Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

This study utilizes satellite-based rainfall CHIRPS to evaluate GCMs-CMIP6 models over Sudan from 1985 to 2014. Overall, the GCMs of BCC-CSM2-MR, CAMS-CSM1-0, CESM2, ECEarth3-Veg, GFDL-ESM4, MIROC-ES2L, and NorESM2-MM are well reproduced in the unimodal pattern of June to September (JJAS), and hence employed to calculate Multi-Model Ensemble (MME). Then, we examine the capability of the GCMs and MME in replicating the precipitation patterns on annual and seasonal scales over Sudan using numerous ranking metrics, including Pearson Correlation Coefficient (CC), Standard Deviation (SD), Taylor Skill Score (TSS), Mean Absolute Error (MAE), absolute bias (BIAS), and, normalized mean root square error (RMSD). The results show that the MME has the lowest bias and slightly overestimates rainfall over most parts of our study domain, whilst, others (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, FGOALS-f3-L, FGOALS-g3) consistently overestimate rainfall in referring to CHIRPS data, respectively, but FIO-ESM-2-0 underestimates bias value. Moreover, MIROC-ES2L and NorESM2-MM demonstrate better performance than the other models. Finally, we employed a bias correction (BC) technique, namely Delta BC, to adjust the GCMs model products through the annual and monsoon seasons. The applied bias correction technique revealed remarkable improvement in the GCMs against the observations, with an improvement of 0 - 18% over the original. However, MME and MIROC-ES2L show better performance after correction than other models.

Drivers of Air‐Sea CO2 Flux in the Subantarctic Zone Revealed by Time Series Observations

AbstractThe subantarctic zone is an important region in the Southern Ocean with respect to its influence on air‐sea CO2 exchange and the global ocean carbon cycle. However, understanding of the magnitude and drivers of the flux are still being refined. Using observations from the Southern Ocean Time Series (SOTS) station (∼47°S, 142°E) and auxiliary data, we developed a multiple linear regression model to compute the sea surface partial pressure of CO2 (pCO2) over the past two decades. The mean amplitude of the pCO2 seasonal cycle between 2004 and 2021 was 44 μatm (range 30–54 μatm). Summer minima ranged from 310 to 370 μatm and winter maxima were near equilibrium with the atmosphere. The non‐thermal (i.e., biological processes and mixing) contribution to the seasonal variability in pCO2 was several times larger than the thermal contribution. The SOTS region acted as a net carbon sink at annual time scales, with mean magnitude of 6.0 mmol m−2 d−1. The positive phase of the Southern Annular Mode (SAM) increased ocean carbon uptake primarily through an increase in wind speed at zero time lag. Increased surface pCO2 was correlated with a positive SAM with a lag of 4 months, mainly due to reduced biological uptake and increased mixing. During the autotrophic season, pCO2 was predominantly impacted by primary productivity, whereas water mass movement, inferred by temperature and salinity anomalies, had a larger impact on the heterotrophic season. In general, mesoscale processes such as eddies and frontal movement impact the local biogeochemical features more than the SAM.

Analysis of the Impact of Drought Characteristics Change on Natural Ecological Environment in the West of Hexi Corridor

The plant communities and wild animals in the Gobi desert outside the oases in the west of Hexi Corridor are an indispensable and important part of the human living environment. Affected by global warming, the spatial and temporal distribution patterns of precipitation have changed significantly. This article uses daily precipitation data from 6 hydro-logical stations in the middle and lower reaches of the Shule River Basin from 1957 to 2018, and daily precipitation data from 3 meteorological stations from 1980 to 2018. Through research on annual scale precipitation anomaly percentage, effective precipitation days and annual distribution, and drought station frequency ratio and other parameters, it is shown that there is a significant change in precipitation distribution within the year. The distribution of effective precipitation is not conducive to the survival of ecosystems, and seasonal drought is on the rise. The trend of continuous drought in two seasons and three seasons is on the rise, and the evolution of drought has a significant impact on the natural ecological environment. Effective rainfall is a key element in the rise and fall, distribution, and change of natural ecosystems. Studying the impact of changes in drought characteristics on natural ecology is of great significance for actively responding to drought disasters and maintaining basic ecology.

Assessment of desertification in the brazilian semiarid region using time series of climatic and biophysical variables

The municipality of Irauçuba, located in the Semiarid region of Ceará, Brazil, has faced the effects of desertification resulting from the scarcity of rainfall. This study aimed to assess the progression of desertification in the area from 2003 to 2020, using climatic and biophysical data collected during this period. We used supplementary data from automatic meteorological stations operated by the National Agency of Water and Basic Sanitation to determine the rainfall anomaly index of the historical series. Additionally, we performed annual scale validation of the CHIRPS data using statistical indicators such as Pearson Correlation Coefficient, Mean Error, Percentage BIAS, and Root Mean Square Error. Subsequently, we analyzed land cover and land use changes over the 17 years (2003-2020) in the municipality by processing CHIRPS, MODIS product (MOD16A2 and MOD11A2), and MapBiomas data in the Google Earth Engine digital platform and QGIS software. We analyzed land cover and land use changes using MapBiomas data. Finally, we analyzed the aridity index for the same 17-year period. The research findings revealed a significant correlation between rainfall and biophysical parameters, with the lowest aridity index of 0.255 observed between 2012 and 2014 and the highest of 0.464 between 2018 and 2020. The area's susceptibility to the desertification process was classified as high, which is consistent with the Semiarid climate of the region. The methodology employed during the research proved to be a sustainable technology of low economic cost and telling and easy access, which enabled the creation of a scientific contribution for future works in the region, besides being able to add knowledge for studies of environmental degradation.

Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai

Building energy consumption is an essential source of greenhouse gas (GHG) and air pollution. Green roofs can directly absorb ambient CO2 and remove air pollutants through their vegetation layers, but a limited number of studies have examined their effects on GHG and air pollutant reduction associated with building energy savings, especially in the context of climate change. This research examined the performance of green roofs on CO2 and air pollutant reduction, including SO2, PM2.5, and NOx, through building energy demand savings in Shanghai, China. Climate change mitigation effects were assessed based on the energy consumption of five types of buildings before and after the installation of green roofs under 2020 and 2050 climate conditions, respectively. EnergyPlus software 9.5.0 was applied to simulate hourly energy consumption for different building prototypes with and without green roofs. Green roofs on all building types exhibited positive energy savings on annual, monthly, and diurnal scales, and they can save more energy for most of the building types under the projected 2050 climate condition. Moreover, most of the building energy saved by green roofs came from the Heating, Ventilation, and Cooling (HVAC) systems. In addition, this study discovered that the energy-saving benefits of green roofs vary based on the type of building they were installed on. Green roofs were found to have the largest energy saving on the shopping mall, especially on extremely hot summer days. Finally, a Geographic Information System (GIS)-based approach was developed with the ability to quantify the amount of GHG and air pollutant reduction associated with building energy savings for existing buildings in the Huangpu District of Shanghai. This approach was also utilized to present the spatial distribution of buildings with different levels of suitability to install green roofs by considering their location attributes and air pollutant reduction potential together, which is the major innovation of this research. The purpose of this study is to provide valuable guidance to policy makers regarding the performance of green roofs in building energy-saving and air quality improvement in the urban environment when facing the challenge of climate change, which is essential for urban sustainability.

The 1991–2020 sea surface temperature normals

AbstractThe 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (&lt;1.0°C) except in frontal zones (&gt;1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020.

Identification of ecologically sensitive zones affected by climate change and anthropogenic activities in Southwest China through a NDVI-based spatial-temporal model

Revealing the driving mechanism of climatic change and anthropogenic activities on vegetation variation and identifying the ecologically sensitive zones in Southwest China are critical for formulating regional ecological restoration strategies. Herein, a NDVI-based spatial–temporal model in Southwest China from 2000 to 2021 under the influences of climatic change and anthropogenic activities alone and in combination was determined based on MODIS NDVI and in situ meteorological data using Theil-Sen median analysis, the Mann–Kendall significance test, residual analysis, and relative analysis. The results showed that on the annual scale, the NDVI, predicted NDVI, and residual NDVI significantly increased at rates of 0.0016 yr−1, 0.0008 yr−1, and 0.0008 yr−1 in Southwest China from 2000 to 2021, respectively. Moreover, the NDVI, predicted NDVI, and residual NDVI significantly (P < 0.05) increased in the Guangxi Hills (GXH), Hengduan Mountains (HDM), Sichuan Basin (SCB), Yunnan-Guizhou Plateau (YGP), Zoige Plateau (ZP) and Tibetan Plateau (TP). The areas with an increase in the NDVI, predicted NDVI, and residual NDVI accounted for 81.45 %, 79.73 %, and 71.20 % of Southwest China, respectively. The NDVI variation in Southwest China was dominated by the combined effects of climatic change and anthropogenic activities, accounting for 83.22 % of the total study area. In addition, 62.76 % of the study area exhibited a greening trend. The contributions of climatic change were greater than those of anthropogenic activities for greening areas, and the opposite is true for browning areas. Innovatively, ecologically sensitive zones were identified through the Theil-Sen median analysis and Mann–Kendall significance test and accounted for 16.78 % of the total study area. Overall, this study proposed a NDVI-based spatial–temporal mode to investigate the driving mechanism of driving forces on NDVI variation, identify ecologically sensitive zones and suggest a management framework in Southwest China.

The synthesis of potential factors contributing to the asynchronous warming between air and shallow ground since the 2000s on the Tibetan Plateau

Atmospheric conditions, topsoil properties and land cover conditions play essential roles in ground surface temperature (GST), surface air temperature (SAT) and their differences (GST-SAT). They determine the strength of the thermal forcing of the lower atmospheric boundary and the distributions of frozen ground in cold regions. However, the relative importance of these factors at various time scales and the underlying physical mechanisms remain less well understood. Here, we investigate the spatiotemporal patterns of GST-SAT and examine 11 potential factors in three categories in influencing the GST-SAT variations from 1983 to 2019 over the Tibetan Plateau (TP) using boosted regression tree models. The results show that the TP has experienced asynchronous warming in GST and SAT since 2001: a warming hiatus in SAT but continued warming in GST, resulting in a significantly increasing trend in GST-SAT. The relative importance of the three categories that influence the GST-SAT spatial variation was: atmospheric variables (56.1 %) > shallow soil properties (24.4 %) > interfacial land cover features (19.5 %). The importance of the factors also varied with the combinations of annual, seasonal, daily, day-time and night-time time scales, manifested by positive or negative effects. The interdecadal changes of net radiation, precipitation, wind speed and soil moisture amplified the asynchronous warming between air and shallow ground over the TP since the 2000s. These findings provide an in-depth understanding of the spatiotemporal variations of GST-SAT and the underlying mechanisms. This study will benefit the development of the Earth system models on the TP.

Influence of drought and minimum temperature on tree growth and water use efficiency of Mediterranean species

The world's forests are currently facing the impacts of climate change and associated extreme events, which are adversely affecting natural ecosystems and increasing risks of forest mortality. In the Mediterranean region, where water is already a limiting factor, drought stress is having a severe impact on tree growth. Different species have developed physiological mechanisms to cope with drought, and an increase in water use efficiency has been observed in several drought-tolerant species. This paper analysed the growth trends and the ecophysiological responses of four widespread Mediterranean species (Pinus pinaster Aiton, Pinus pinea L., Pinus halepensis Mill and Quercus ilex L.) to climate variability, in a natural reserve of the Vesuvio National Park (Southern Italy). Dendrochronological analyses were used to measure tree-ring widths and to estimate basal area increments for each species. Stable isotope analyses of δ13C were performed to calculate intrinsic water use efficiency on an annual scale for the four species. Climate-growth relationships suggested that temperatures, in particular annual minimum temperature, had a significant impact on the growth of each species. The species that showed the highest resistance to hot periods and water scarcity was Pinus halepensis, presenting the highest intrinsic water use efficiency values. On the other hand, Quercus ilex was found to be the most susceptible to prolonged drought periods and sensitive to changes in precipitation, resulting in the weakest growth rates during extreme heat and cold periods. Our study demonstrated the importance of better understanding the ecophysiological responses of each species to develop sound conservation and forest management plans.

Annual to seasonal glacier mass balance in High Mountain Asia derived from Pléiades stereo images: examples from the Pamir and the Tibetan Plateau

Abstract. Glaciers are crucial sources of freshwater in particular for the arid lowlands surrounding High Mountain Asia. To better constrain glacio-hydrological models, annual, or even better, seasonal information about glacier mass changes is highly beneficial. In this study, we evaluate the suitability of very-high-resolution Pléiades digital elevation models (DEMs) to measure glacier mass balance at annual and seasonal scales in two regions of High Mountain Asia (Muztagh Ata in Eastern Pamirs and parts of western Nyainqêntanglha, south-central Tibetan Plateau), where recent estimates have shown contrasting glacier behaviour. The average annual mass balance in Muztagh Ata between 2019 and 2022 was −0.07 ± 0.20 m w.e. a−1, suggesting the continuation of a recent phase of slight mass loss following a prolonged period of balanced mass budgets previously observed. The mean annual mass balance in western Nyainqêntanglha was highly negative for the same period (−0.60 ± 0.15 m w.e. a−1), suggesting increased mass loss rates compared to the approximately previous 5 decades. The 2022 winter (+0.13 ± 0.24 m w.e.) and summer (−0.35 ± 0.15 m w.e.) mass budgets in Muztagh Ata and western Nyainqêntanglha (−0.03 ± 0.27 m w.e. in winter; −0.63 ± 0.07 m w.e. in summer) suggest winter- and summer-accumulation-type regimes, respectively. We support our findings by implementing the Sentinel-1-based Glacier Index to identify the firn and wet-snow areas on glaciers and characterize the accumulation type. The good match between the geodetic and Glacier Index results supports the potential of very-high-resolution Pléiades data to monitor mass balance at short timescales and improves our understanding of glacier accumulation regimes across High Mountain Asia.