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Current‐generation climate models project that Africa will warm by up to 5°C in the coming century, severely stressing African populations. Past and ongoing work indicates, however, that the models used to create these projections do not match proxy records of past temperature in Africa during the mid‐Holocene (MH), raising concerns that their future projections may house large uncertainties. Rather than reproducing proxy‐based reconstructions of MH warming relative to the Pre‐Industrial (PI), models instead simulate MH temperatures very similar to or slightly colder than the PI. This data‐model mismatch could be due to a variety of factors, including biases in model surface energy budgets or inaccurate representation of the feedbacks between temperature and hydrologic change during the "Green Sahara." We focus on the differences among model simulations in the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3 and PMIP4), examining surface temperature and energy budgets to investigate controls on temperature and the potential model sources of this paleoclimate data‐model mismatch. Our results suggest that colder conditions simulated by PMIP3 and PMIP4 models during the MH are in large part due to the joint impacts of feedback uncertainties in response to increased precipitation, a strengthened West African Monsoon (WAM) in the Sahel, and the Green Sahara. We extend these insights into suggestions for model physics and boundary condition changes, and discuss implications for the accuracy of future climate model projections over Africa. Key Points: We evaluate the simulation of African air temperatures in Paleoclimate Modeling Intercomparison Project Phases 3 and 4 simulations of the mid‐HoloceneEnergy balance decomposition analyses indicate the hydrologic cycle plays a key role in causing mid‐Holocene cooling in model simulations"Green Sahara" experiments show that dust and vegetation affect simulated temperatures, revealing pathways for refining model simulations [ABSTRACT FROM AUTHOR]

Spring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high‐latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk‐surface layer. Energy fluxes across this bulk surface and the snow‐soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade‐long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10‐day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk‐surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework. Plain Language Summary: It is essential to understand why and how spring snowmelt occurs in boreal forests because of its impact on both the biological and hydrological processes in the ecosystem. However, meteorological drivers related to snowmelt often change simultaneously, which makes it challenging to determine which single driver is vital for snowmelt. To address this challenge, we constructed a simple snowmelt model that treats the ecosystem surface as a single bulk layer. The simple model bypasses the needs for detailed canopy information and is easily constrained by observed data. Applying the model to decadal observation data from Alaskan boreal forests, we found that snowfall, air temperature, and atmospheric radiation were important meteorological drivers for explaining interannual variations in snowmelt timing. Each of them explained about 1–2 weeks of snowmelt disappearance dates. We also demonstrated that the model helped explain why the snowmelt was late in 2013 and early in 2016 and 2019 and why snow disappearance dates differed between study sites. While a simple model has its limitations, it can be beneficial for understanding snowmelt characteristics within a simple theoretical framework. Key Points: A model‐based approach was introduced to delineate the roles of meteorological drivers on interannual variations in snowmelt timingSnowfall was the most important driver of the interannual variation in snowmelt timing followed by air temperature and atmospheric radiationLate snowmelt in 2013 was attributed to low air temperature, increasing sensible heat loss from the snowpack [ABSTRACT FROM AUTHOR]

Solar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts. Plain Language Summary: The Tibetan Plateau (TP) is characterized by high elevation and complex topography. Interaction between solar radiation and the undulating topography has important impacts on the regional surface energy balance and hydrologic cycle. Here we use Earth System Model simulations to show the local and remote impacts of the TP's solar radiation‐topography interaction on the surface climate of the Asian regions. Such interaction overall increases the air temperature especially in winter over the TP and reduces the summer precipitation in southern TP. Teleconnectedly, the interaction further alters the precipitation patterns in South and East Asia, by altering the atmospheric circulation that influences moisture transport and clouds. Accounting for such interaction generally improves the model performance when benchmarked against observations. These findings underscore the important roles of the TP's solar radiation‐topography interaction in modulating the climate of the local and remote Asian regions. Key Points: Solar radiation‐topography interaction over the Tibetan Plateau (TP) increases annual average regional near‐surface air temperature by 0.26 KSolar radiation‐topography interaction over the TP also affects the precipitation patterns in South and East AsiaIncluding solar radiation‐topography interaction improves the simulation of surface climate over the TP and Asian regions [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important part of the water cycle and influences, for example, water management and agricultural production. The article presents temporal and spatial changes in reference evapotranspiration (ETo) in the growing season (April–September) against the background of climatic conditions in Poland in the years 1966–2020. ETo was calculated using the Penman–Monteith method using data from 37 meteorological stations in the network of the Institute of Meteorology and Water Management‐National Research Institute. For the growing season of April–September, the average sum of ETo for Poland was 529 mm. In terms of spatial distribution, the values were lowest in northern Poland (Kołobrzeg, 480 mm; Lębork, 488 mm) and highest in the central lowlands (Warsaw, 570 mm; Poznań, 566 mm). For individual months, the average sum of ETo ranged from 53 to 63 mm in April and September to 108–112 mm in June and July. In Poland, in the years 1966–2020, ETo showed a statistically significant upward trend of 16.4 mm/10 year. This is the result of, inter alia, statistically significant increases in sunshine duration and air temperature and a decrease in relative humidity (respectively: 62 h/10 year, 0.40°C/10 year and −1%/10 year) during the growing season in Poland. Within the multiyear study period, the maximum ETo values occurred in 2018, reaching values of >700 mm. At that time, Poland was under the influence of very intense anticyclonic circulation. This is confirmed by the relationship between ETo and the anticyclonic circulation index for central Poland, which is statistically significant for the 55 study years. [ABSTRACT FROM AUTHOR]

Reliable precipitation simulation over the Tibetan Plateau (TP) remains a challenge, manifested by a prominent systematic wet bias in the warm season. Previous studies have generally neglected the potential linkage between surface radiation energy budget and precipitation bias. Prevalent scattered cumulus and thunderstorms over the TP in summer strongly influence surface radiation. A cloud fraction scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF model and tested for a month‐long simulation. It is found that the scheme better reproduces the surface solar radiation compared to a default cloud fraction scheme in the WRF model. Using abundant surface observations, we find that overestimation of the downward surface shortwave radiation (DSSR) would lead to wet bias. DSSR overestimation contributes to higher surface temperature and larger evaporation and enhanced atmospheric instability, which favor more simulated convective precipitation. The study suggests that a better simulation of clouds and surface radiation would benefit precipitation simulation over the plateau. Plain Language Summary: Summer precipitation over the Tibetan Plateau (TP) is generally overestimated in general circulation models as well as regional weather forecasts and climate models. The prevailing "popcorn‐like" clouds over the TP in summer would strongly impact surface radiation. Considering the potential linkage between surface radiation energy budget and precipitation bias, a statistical cloud fraction and subgrid condensation scheme (GS‐PDF scheme) is implemented in the WRF model and tested for a month‐long simulation. The GS‐PDF scheme better captures clouds over the TP with more mid‐low cloud water (ice) than the default Xu‐Randall scheme. Increased mid‐low cloud water in the GS‐PDF scheme reduced simulated downward surface shortwave radiation, reduced simulated surface temperatures, atmospheric instabilities and convective precipitation, leading to alleviated wet bias over the TP. This study suggests that better simulations of cloud and surface radiation would be beneficial for simulations of hydrological cycles over the TP. Key Points: A new cloud macrophysics scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF modelThe new macrophysics scheme reduced simulated downward surface shortwave radiation, surface temperatures and atmospheric instabilitiesA better simulation of clouds and surface radiation would benefit precipitation simulation over the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Evaporation is controlled by soil moisture (SM) availability when conditions are not extremely wet. In such a moisture‐limited regime, land‐atmosphere coupling is active, and a chain of linked processes allow land surface anomalies to affect weather and climate. How frequently any location is in a moisture‐limited regime largely determines the intensity of land feedbacks on climate. Conventionally this has been quantified by shifting probability distributions of SM, but the boundary between moisture‐limited and energy‐limited regimes, called the critical soil moisture (CSM) value, can also change. CSM is an emergent property of the land‐atmosphere system, determined by the balance of radiative, thermal and kinetic energy factors. We propose a novel framework to separate the contributions of these separate effects on the likelihood that SM lies in the moisture‐limited regime. We confirm that global warming leads to a more moisture‐limited world. This is attributed to reduced SM in most regions: the moisture effect. CSM changes mainly due to shifts in the surface energy budget, significantly affecting 27.7% of the globe in analyzed climate change simulations. However, consistency among Earth system models regarding CSM change is low. The poor agreement hints that variability of CSM in models and the factors that determine CSM are not well represented. The fidelity of CSM in Earth system models has been overlooked as a factor in water cycle projections. Careful assessment of CSM in nature and for model development should be a priority, with potential benefits for multiple research fields including meteorology, hydrology, and ecology. Plain Language Summary: In the water cycle, moisture‐limited conditions exist when evaporation is limited by a lack of soil moisture. This occurs when soil moisture lies below a threshold called the critical soil moisture (CSM). As evaporation affects atmospheric temperature and humidity, the value of CSM is important for weather and climate, as it determines when land states can affect the atmosphere. Climate change simulations agree the world will become more moisture‐limited, mainly attributed to drying soils, but the value of CSM can also change because it is determined in part by local meteorology as part of a land‐atmosphere feedback. This study shows that simulations from different climate change models consistently agree on an overall drying of the soil in the future. Changes in CSM are also simulated, but Earth system models do not agree on the magnitude or direction of CSM change in most places. This disagreement introduces uncertainty in the places and times when soil moisture controls evaporation and its impact on the atmosphere. Models have not historically been calibrated or validated for CSM simulation; we advocate for more attention to be paid to observing and modeling CSM due to its importance for meteorology, hydrology, and ecology in a changing climate. Key Points: An increasingly moisture‐limited world under global warming depends on more than just reduced soil moistureEarth system models inconsistently simulate the critical soil moisture value that separates moisture‐limited and energy‐limited regimesPoor agreement among models on projected changes in critical soil moisture calls for greater focus on its observation and validation [ABSTRACT FROM AUTHOR]

The Mars 2020 mission rover "Perseverance", launched on 30 July 2020 by NASA, landed successfully 18 February 2021 at Jezero Crater, Mars (Lon. E 77.4509° Lat. N 18.4446°). The landing took place at Mars solar longitude Ls = 5.2°, close to start of the northern spring. Perseverance's payload includes the relative humidity sensor MEDA HS (Mars Environmental Dynamics Analyzer Humidity Sensor), which operations, performance, and the first observations from sol 80 to sol 410 (Ls 44°–210°) of Perseverance's operations we describe. The relative humidity measured by MEDA‐HS is reliable from late night hours to few tens of minutes after sunrise when the measured humidity is greater than 2% (referenced to sensor temperature). Data delivered to the Planetary Data System include relative humidity, sensor temperature, uncertainty of relative humidity, and volume mixing ratio (VMR). VMR is calculated using the MEDA‐PS pressure sensor values. According to observations, nighttime absolute humidity follows a seasonal curve in which release of water vapor from the northern cap with advancing northern spring and summer is visible. At ground level, frost conditions may have been reached a few times during this season (Ls 44°–210°). Volume mixing ratio values show a declining diurnal trend from the midnight toward the morning suggesting adsorption of humidity into the ground. Observations are compared with an adsorptive single‐column model, which complies with observations and confirms adsorption. The model allows estimating daytime VMR levels. Short‐term subhour timescales show large temporal fluctuations in humidity, which suggest vertical and spatial advection. Plain Language Summary: The Mars 2020 mission rover "Perseverance" landed successfully on 18 February 2021 at Jezero Crater, Mars. The rover's payload includes a versatile instrument suite which includes a relative humidity sensor, whose observations for the first 410 Martian days are described here. The observations show how the lowest level of atmosphere is generally dry but still exceeding saturation is feasible because of cold nights. Sensor operations and accuracy estimates are presented. Relative humidity together with MEDA pressure and air temperature observations allow calculating absolute water vapor content of air at the sensor level at nighttime. Humidity observations are also compared with models describing water vapor adsorption and desorption into and out from soil. The results show how atmospheric humidity at the rover's site experiences large subhour variability. Humidity observations help to understand interchange of humidity between the soil and the atmosphere. Water is mandatory for life, such as on earth, thus understanding these water cycle processes better are important for evaluating possibilities of past and current habitability of Mars. Perseverance is also collecting samples which maybe returned to Earth one day. Knowledge of the conditions at the times when samples were collected maybe useful. Key Points: Humidity observations in Mars by M2020 Perseverance rover during the first 410 sols of operation are shown and discussedHumidity sensor MEDA‐HS operations and sensor accuracy are explainedAdsorptive single column model is tested and compared with humidity observations [ABSTRACT FROM AUTHOR]

Anthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we integrate bias-corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen-Geiger climate zones. Results show that the total frequency of hydrological droughts is likely to stay unchanged while extreme hydrological droughts (e.g., events with a 30 yr joint return period, JRP) are projected to occur more frequently across the 21st century. The historical 30 yr JRP events assessed during the historical baseline period of 1985–2014 could become twice as frequent over ∼60% of global catchments by 2071–2100 under the middle and high emission scenarios (ESs). Climate uncertainty (i.e., from global climate models and ESs) is the major source of uncertainty over temperate and tropical catchments, versus HY uncertainty in arid catchments with locally complex runoff regimes. Our machine learning framework indicates that precipitation stress controls the development of historical droughts over ∼87% of global catchments. However, with climate warming, air temperature variations are expected to become the new primary driver of droughts in high-latitude cold catchments. This study highlights an increasing risk of global extreme hydrological droughts with warming and suggests that rising temperatures in high latitudes may lead to more extreme hydrological droughts. [ABSTRACT FROM AUTHOR]

It is certain that globally the troposphere has warmed since the industrial revolution because of the greenhouse gas emissions. The water vapour content of air increases as the climate warms. However, there is nonsignificant long‐term trend in global‐scale precipitation. This study aims to investigate the sources of the uncertainty of the long‐term precipitation trend under global warming through the water cycle. The data of the historical simulations (1850–2005) from 30 CMIP5 models are used in the estimate. The p‐value is utilized to indicate the significance of the long‐term trend of the water‐vapour‐related variable in the water cycle as well as the air temperature near surface and at the condensation level. For each variable, the p‐value is calculated for each of the grid points. The overall spatial distribution patterns of the p‐values are compared between two variables that linked directly, and a point‐to‐point comparison is also performed to the two fields. Results show that over the globe, the near‐surface specific humidity increases significantly at most of the grid points while the increase of the specific humidity at the condensation level is significant at some, and, however, the long‐term trend of the relative humidity at the condensation level is majorly nonsignificant. This suggests that as assessed from the global grid points, the uncertainty of the long‐term trend of the water‐vapour‐related variable enlarges substantially in the process of condensation, and modestly in the process of the transport of water vapour. Therefore, the uncertainty of the long‐term precipitation trend under global warming is mainly attributed to the condensation and slightly due to the transport of water vapour. The results obtained from the 30 models are consistent with each other, which supports the reliability of the conclusion. [ABSTRACT FROM AUTHOR]

Summary: Understanding the effects of temperature and moisture on radial growth is vital for assessing the impacts of climate change on carbon and water cycles. However, studies observing growth at sub‐daily temporal scales remain scarce.We analysed sub‐daily growth dynamics and its climatic drivers recorded by point dendrometers for 35 trees of three temperate broadleaved species during the years 2015–2020. We isolated irreversible growth driven by cambial activity from the dendrometer records. Next, we compared the intra‐annual growth patterns among species and delimited their climatic optima.The growth of all species peaked at air temperatures between 12 and 16°C and vapour pressure deficit (VPD) below 0.1 kPa. Acer pseudoplatanus and Fagus sylvatica, both diffuse‐porous, sustained growth under suboptimal VPD. Ring‐porous Quercus robur experienced a steep decline of growth rates with reduced air humidity. This resulted in multiple irregular growth peaks of Q. robur during the year. By contrast, the growth patterns of the diffuse‐porous species were always right‐skewed unimodal with a peak in June between day of the year 150–170.Intra‐annual growth patterns are shaped more by VPD than temperature. The different sensitivity of radial growth to VPD is responsible for unimodal growth patterns in both diffuse‐porous species and multimodal growth pattern in Q. robur. [ABSTRACT FROM AUTHOR]

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local‐to‐regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento‐San Joaquin Delta, California, USA, as well as leaf‐level T data for validation. The four wetlands vary in their vegetation make‐up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R2 across all sites. The order of variable inclusion (and importance) was: VPD > gap‐filled sensible heat flux (H_gf) > air temperature (Tair) > friction velocity (u∗) > other variables. The model using VPD, H_gf, Tair, and u∗ showed the best performance during validation with independent data and had a mean testing R2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO2 exchange data especially for more heterogeneous sites with large E contributions. Our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning. [ABSTRACT FROM AUTHOR]

While large‐scale terrestrial evapotranspiration (ET) information is essential for our understanding of the Earth's water and energy cycles, substantial differences exist in current global ET products due partly to uncertainties in soil‐ and vegetation‐related parameters and/or precipitation forcing. Here a calibration‐free complementary relationship (CR) model, driven purely by routine meteorological forcing (air and dew‐point temperature, wind speed, and net radiation), mainly from ERA5, was employed to estimate global ET rates during 1982–2016. Modeled ET agrees favorably with (a) monthly eddy‐covariance measurements of 129 global FLUXNET sites, and; (b) water‐balance‐derived ET of 52 basins at the multiyear mean and annual scales. Additional evaluations demonstrate that the CR is very competitive, in comparison with other 12 widely used global ET products. The 35‐years mean global land ET rate from the CR is 500 ± 6 mm yr−1 (72.3 ± 0.9 × 103 km3 yr−1) with more than 70% of the land area exhibiting increasing annual ET rates over the study period. Globally, CR ET significantly increased at a rate of 0.31 mm yr−1 during 1982–2016, suggesting a 2.2% increase in global land ET over last 35 years. Model inter‐comparisons indicate that global annual CR ET values and their trend are close to the median of not only the 12 ET products chosen but also that of 20 CMIP6 models. Since this calibration‐free CR model requires no precipitation (except in sea‐shore deserts for a subsequent ET correction), vegetation or soil data, it could be incorporated into complex hydrological and/or climate models, thereby facilitating large‐scale hydrological and climate simulations. Key Points: A global, 30‐years‐plus complementary relationship evapotranspiration (ET) product is developed and validated at the plot and basin scalesThis new ET product, derived from a minimal number of inputs without vegetation or soil data, may improve upon previous global ET estimatesGlobal terrestrial ET rates increased significantly during 1982–2016, particularly in the Northern Hemisphere [ABSTRACT FROM AUTHOR]

Water vapour is among the most critical variables that shape the Earth's energy balance and hydrologic cycle. Surface relative humidity (RH) and specific humidity (SH) are the main measures of atmospheric moisture. Long‐term changes in these variables in South Korea vary regionally as well as locally, but have not been explored using long‐term observation data. This study analyzes the annual trends of the means and SD of RH, surface air temperature, and SH in South Korea at 55 stations for the period 1973–2018. Three trend detection tests based on the Mann–Kendall test have been performed to detect trends at the 5% significance level for the annual and monthly statistics (means and SD) of these variables. Significant decreasing and increasing trends have been detected in the annual means of RH and air temperature, while the annual mean of SH has remained nearly constant. Overall, the surface air in South Korea has become drier over the study period, particularly in winter. The trend in RH and SH varies widely according to months to result in the increase in the SD over the annual cycle. The monthly SD of RH, SH, and surface air temperature show especially large positive trends for March and September, when the transition from winter to spring and summer to fall, respectively, occurs. [ABSTRACT FROM AUTHOR]

The Langtang catchment is a high mountain, third order catchment in the Gandaki basin in the Central Himalaya (28.2°N, 85.5°E), that eventually drains into the Ganges. The catchment spans an elevation range from 1400 to 7234 m a.s.l. and approximately one quarter of the area is glacierized. Numerous research projects have been conducted in the valley during the last four decades, with a strong focus on the cryospheric components of the catchment water balance. Since 2012 multiple weather stations and discharge stations provide measurements of atmospheric and hydrologic variables. Full weather stations are used to monitor at an hourly resolution all four radiation components (incoming and outgoing shortwave and longwave radiation; SWin/out and LWin/out), air temperature, humidity, wind speed and direction, and precipitation, and cover an elevational range of 3862–5330 m a.s.l. Air temperature and precipitation are monitored along elevation gradients for investigations of the spatial variability of the high mountain meteorology. Dedicated point‐scale observations of snow cover, depth and water equivalent as well as ice loss have been carried out over multiple years and complement the observations of the water cycle. All data presented is openly available in a database and will be updated annually. [ABSTRACT FROM AUTHOR]

The Jalovecký Creek catchment, Slovakia (area 22.2 km2, mean elevation 1500 m a.s.l.), is likely the last big valley complex in the Carpathian Mountains, in which the hydrological cycle is still governed by natural processes. Hydrological research is conducted there since the end of the 1980s. The overall mission of the research is to increase the knowledge about the hydrological cycle in the highest part of the Carpathians. The research agenda, briefly introduced in the first part of this article, is focused on water balance, snow accumulation and melt and runoff formation. Recent analysis of precipitation, discharge, snow cover and isotopic data from period 1989–2018 indicates that hydrological cycle has become more dynamic since 2014. Although several indicators suggest that it could be related to the cold part of the year, direct links with snow storage and the contribution of snowmelt water to catchment runoff were not confirmed. The second part of the article is therefore focused on an analysis of daily cycles in streamflow in March to June 1988–2018 to obtain a deeper insight into the snowmelt process. We describe characteristics of the cycles and examine their variability over the study period. The results indicate that less snow at the lowest elevations (800–1150 m a.s.l.) since 2009 could have influenced the cessation of the cycles in June since 2010. The possible role of the decreased amount of snow at the lowest elevations in changes in runoff characteristics is also suggested by an increase in time lags between maximum discharges during the events and maximum air temperatures preceding discharge maxima measured near the catchment outlet (at 750 m a.s.l.) in spring 2018 compared to springs with a similar number of streamflow cycles in the years 1988, 2000 and 2009. Wavelet analysis did not indicate changes in global power spectra in hourly discharge and air temperature data. [ABSTRACT FROM AUTHOR]

Summary: Photosynthetic water‐use efficiency (WUE) describes the link between terrestrial carbon (C) and water cycles. Estimates of intrinsic WUE (iWUE) from gas exchange and C isotopic composition (δ13C) differ due to an internal conductance in the leaf mesophyll (gm) that is variable and seldom computed.We present the first direct estimates of whole‐tree gm, together with iWUE from whole‐tree gas exchange and δ13C of the phloem (δ13Cph). We measured gas exchange, online 13C‐discrimination, and δ13Cph monthly throughout spring, summer, and autumn in Eucalyptus tereticornis grown in large whole‐tree chambers. Six trees were grown at ambient temperatures and six at a 3°C warmer air temperature; a late‐summer drought was also imposed.Drought reduced whole‐tree gm. Warming had few direct effects, but amplified drought‐induced reductions in whole‐tree gm. Whole‐tree gm was similar to leaf gm for these same trees. iWUE estimates from δ13Cph agreed with iWUE from gas exchange, but only after incorporating gm. δ13Cph was also correlated with whole‐tree 13C‐discrimination, but offset by −2.5 ± 0.7‰, presumably due to post‐photosynthetic fractionations.We conclude that δ13Cph is a good proxy for whole‐tree iWUE, with the caveats that post‐photosynthetic fractionations and intrinsic variability of gm should be incorporated to provide reliable estimates of this trait in response to abiotic stress. [ABSTRACT FROM AUTHOR]

Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5°. After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C. The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP. [ABSTRACT FROM AUTHOR]

Climate change is intensifying the hydrologic cycle globally, increasing both the size and frequency of extreme precipitation events, or deluges. Arid and semi‐arid ecosystems are expected to be particularly responsive to this change because their ecological processes are largely driven by distinct soil moisture pulses. However, since soil moisture, air temperature and plant phenology vary throughout the growing season, deluges will likely have differing impacts on these systems depending on when they occur.We conducted a field experiment to assess how the seasonal timing (early, middle or late growing season) of a single deluge (70‐mm precipitation event) altered key ecological processes in the semi‐arid shortgrass steppe of North America.Regardless of timing, a single deluge stimulated most ecosystem processes, but a deluge at mid‐season caused the greatest increase in soil respiration, canopy greenness, above‐ground net primary production (ANPP) and growth and flowering of the dominant plant species Bouteloua gracilis. In contrast, below‐ground net primary production (BNPP) was insensitive to deluge timing, with a consistent BNPP increase in all the deluge treatments that was almost twice as large as the ANPP response. This BNPP response was largely driven by enhanced root production at the 10–20 cm, rather than 0–10 cm, soil depth.Synthesis. In a semi‐arid ecosystem, a single deluge can have season‐long impacts on many ecosystem processes, but responses can be mediated by event timing. Therefore, predicting responses of semi‐arid ecosystems to more dynamic precipitation regimes, and subsequent impacts on the global carbon budget, will require knowledge of how deluge magnitude, frequency and timing are being altered by climate change. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key factor in the exchanging process of the hydrological cycle, which is rather difficult to be directly observed. In situ measurements on SM, however, are subjected to the point scales. Use of land surface models has been a promising way to explore SM variations, especially for poorly gauged high mountain regions such as the Yarlung Zangbo River (YZR) basin located in Southeast Tibetan Plateau. This study made an attempt to investigate the spatiotemporal variations of SM and discuss hydro‐meteorological factors impacting SM evolution based on the Global Land Data Assimilation Systems (GLDAS) outputs during the period 1970–2009. Results show that (a) GLDAS data sets have high agreement and low bias with in situ measurements and consistent spatial distribution with ERA reanalysis data sets; (b) an abrupt change of SM is detected in 1992 and a significantly decreasing trend happens during 1970–2009 and 1993–2009; (c) precipitation is the dominant climatic factor controlling SM during the period 1970–2009, whereas surface air temperature is the critical factor for the significant change of SM. Owing to the significant increasing of surface air temperature since 1992, its impact on SM increased by ∼91% than that before 1992. Evapotranspiration (ET) and snow water equivalent (SWE) are also taken into consideration, showing relatively weak influence on SM, which may be due to the dynamic process of coupled SM‐ET or low snow cover area fraction across the YZR basin. Findings in this study have important implications for SM variations in poorly gauged high mountain regions which may largely influences downstream water availability. [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important factors determining the occurrence of extreme hydro‐meteorological events and water resource availability. Precipitation in different grades has diverse ecological effects, and slight precipitation (SP, defined as 0.1–1.0 mm/day) is the minimal level among them. In this study, we investigated SP trends from 1961 to 2013, as well as the relationship between SP and advanced very high radiometric resolution (AVHRR) normalized difference vegetation index (NDVI) in China during growing season from 1981 to 2006. The distributions and trends of SP were analysed by calculating the daily precipitation data. The average annual slight precipitation amount (SPA) and the number of slight precipitation days (SPD), derived from 839 monitoring stations in China, show a decreasing trend over the last five decades, which is in agreement with total precipitation (TP) but in different rates. When the trend was analysed seasonally, SP in most stations decreases significantly in September–October–November (SON) and June–July–August (JJA), and the largest decrease is found in SON. About 49.5 and 68.7% of monitoring stations show a decreasing trend in SON, in both SPA and SPD, whereas the trend is less popular in March–April–May (MAM, SPA: 19.7%, SPD: 41.4%) and December–January–February (JJF, SPA: 25.6%, SPD: 43.1%). Moreover, our analysis indicates that the decrease of SP is mainly due to the decrease of SPD as the median amount of daily SP was unchanged over the past five decades (close to 0.3 mm/day). Based on 26‐year (1981–2006) semi‐monthly AVHRR NDVI data and the records of SP data, the relationship between AVHRR NDVI and SP was also investigated. In regions with lower (<600 mm) TP, the correlation coefficients between NDVI and SP tend to be higher. These results highlight that SP has different effects than TP on vegetation growth. We also analysed time lag effects and concluded that the sensitivity of NDVI to SP for grass vegetation (the correlation coefficient is 0.327) is more noticeable than for trees (0.211) or shrubs (−0.058). The relationship between SP and NDVI also provides us new insights on the dependence of vegetation growth on meteorological factors. Distribution of SP in China and its relationship with natural vegetation. [ABSTRACT FROM AUTHOR]

The study applies the improved cloud-free moderate resolution imaging spectral radiometer daily snow cover product (MODMYD_MC) to investigate the snow cover variations from snow hydrologic year (HY) HY2000 to HY2013 in the Amur River basin (ARB), Northeast Asia. The fractions of forest cover were 38%, 63%, and 47% in 2009 in China (the southern ARB), Russia (the northern ARB), and ARB, respectively. Validation results show that MODMYD_MC has a snow agreement of 88% against in situ snow depth (SD) observations (SD ≥ 4 cm). The agreement is about 10% lower at the forested stations than at the nonforested stations. Snow cover durations (SCDs) from MODMYD_MC are 20 days shorter than ground observations (SD ≥ 1 cm) at the forested stations, whereas they are just 8 days shorter than ground observations (SD ≥ 1 cm) at the nonforested stations. Annual mean SCDs from MODMYD_MC in the forested areas are 21 days shorter than those in the nearby farmland in the Sanjiang Plain. This indicates forest has a complex influence on the snow accumulation and melting processes and even on optical satellite snow cover mapping. Meanwhile, SCD and mean snow cover are negatively correlated with air temperature in ARB, especially in the snow melting season, when mean air temperature in March and April can explain 86% and 74% of the mean snow cover variations in China ARB and Russia ARB, respectively. From 1961 to 2015, the annual mean air temperature presented an increased trend by 0.33 °C/decade in both China ARB and Russia ARB, whereas it had a decrease trend from HY2000 to HY2013. The decrease of air temperature led to an increase of snow cover, which is different from the global decrease trend of snow cover variations. SCD and snow cover had larger increase rates in China ARB than in Russia ARB, and they were larger in the forested areas than in the nearby farmland in the Sanjiang Plain. [ABSTRACT FROM AUTHOR]

Precipitation-phase partitioning methods (PPMs) that are used in simulating cold-region hydrological processes vary significantly. Typically, PPMs are based on empirical algorithms that are driven by readily available near-surface air temperature but ignore the physical processes controlling precipitation phase by not incorporating humidity. Because these lack any physical basis, there is uncertainty in their spatial and temporal transferability. Recently, humidity-based methods that have a stronger physical basis and smaller uncertainty have been developed. To quantify the uncertainty that empirical PPMs introduce into hydrological simulations, a cold-region hydrological modelling platform was used with a physically based PPM and a selection of empirical PPMs to calculate a set of snow regime and streamflow regime indices. The empirical PPMs included a single air temperature threshold and a double air temperature threshold, whereas the physically based PPM used a psychrometric energy balance model. All calculations were run with near-surface meteorological observations that typically drive hydrological models. Intercomparison of the hydrological responses to the PPMs highlighted substantial differences between the wide range of responses to empirical algorithms and the very small uncertainty due to physically based methods. Uncertainty of hydrological processes, quantified by simulating over a range of air temperature thresholds, reached 20% for the rainfall fraction, 0.4 mm/day for basin discharge, 160 mm of peak snow water equivalent, 36 days for hydrological uncertainty snow cover duration, 26 days for snow-free date and 10 days for peak discharge date. The implication of this research is that the reduced uncertainty derived from implementing physically based PPMs, for operational or research purposes, are greatest for snowpack prediction in mountain basins. However for streamflow discharge calculations, the reduced uncertainty was greatest in prairie and alpine basins due to the additional effects of precipitation phase calculations on frozen soil infiltration and summer snowmelt processes respectively. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Estimating the thermal response of streams to a warming climate is important for prioritizing native fish conservation efforts. While there are plentiful estimates of air temperature responses to climate change, the sensitivity of streams, particularly small headwater streams, to warming temperatures is less well understood. A substantial body of literature correlates subannual scale temperature variations in air and stream temperatures driven by annual cycles in solar angle; however, these may be a low-precision proxy for climate change driven changes in the stream energy balance. We analyzed summer stream temperature records from forested streams in the Pacific Northwest for interannual correlations to air temperature and standardized annual streamflow departures. A significant pattern emerged where cold streams always had lower sensitivities to air temperature variation, while warm streams could be insensitive or sensitive depending on geological or vegetation context. A pattern where cold streams are less sensitive to direct temperature increases is important for conservation planning, although substantial questions may yet remain for secondary effects related to flow or vegetation changes induced by climate change. [ABSTRACT FROM AUTHOR]

The partition of available energy into evapotranspiration affected by environmental and physiological factors is critical in understanding the water cycle and optimizing the water management in the field. Our study attempted to accurately quantify the environmental and physiological control on variability in evaporative fraction ( EF) based on the Penman-Monteith model. The eddy covariance method was used to measure water flux over a canopy and then calculate the EF above a maize field in northwest China in 2007. Results indicate that the EF was lower in other growth stages than in the heading stage, so the EF value in the heading stage was taken as the standard value. The decreases in EF caused by canopy conductance and environmental factors were 0.176 and −0.026, accounting for 117% and −17% of the total difference in EF, respectively. Such results were mainly due to the following: (1) the variation of maize canopy conductance was greater than that of the environmental factors, such as air temperature, air humidity, wind speed and radiation; (2) the EF of the maize was more sensitive to variation in canopy conductance than the environmental factor. Our study revealed that crop physiological factor played an important role in determining the energy partition processes and reducing the sensible heat flux in the maize field. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents the DRAINMOD-FOREST, a hybrid and stand-level forest ecosystem model, to stimulate the nitrogen (N), carbon (C) and hydrology dynamics and tree growth for drained forest lands and usual silvicultural practices. The forest growth model estimates litterfall, C allocation and net primary production with the use of physiology-based methods regulated by soil N conditions, stand age, water deficit and air temperature. The performance of the newly developed DRAINMOD-FOREST model was measured with the use of a long-term data set gathered from an artificially drained loblolly pine (Pinus taeda L.) plantation in eastern North Carolina.

The diurnal hydrologie cycle, a sequence of évapotranspiration, boundary layer growth, moist convection, and precipitation, is described in a thermodynamic framework, assuming an atmosphere composed solely of water. This idealized cycle is shown to be equivalent to an abbreviated version of the classical Rankine cycle where not all the water vapor is condensed. Energy and entropy fluxes of the processes involved in the cycle are quantified using the reversible approximation as a function of the quality of the liquid-vapor mixture (the ratio of the residual background vapor and the total mass of water) and the different temperatures at which evaporation and condensation take place. The proposed framework allows quantitative estimates of the net work (which is used by the cycle to drive the atmospheric circulation and dissipated by various frictional forces and nonidealities) as well as of the thermodynamic efficiency of the cycle. Possible extensions of the idealized framework relating to the role of dry air and the inclusion of various irreversible processes are also discussed. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to examine the spatial, temporal and frequency patterns of climatic variables in Alberta, and relate them to its eco-climate regions. It explores the use of wavelet analysis, wavelet-based empirical orthogonal function (WEOF) analysis and empirical orthogonal function (EOF) analysis on gridded precipitation, air temperature, potential evapotranspiration and net precipitation. It also evaluates the variability relationship between precipitation and runoff.