Net Longwave Radiation Research Articles

A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018

Longwave radiation components, including downward, upward, and net longwave radiation (DLR, ULR, and NLR, respectively), are essential parameters in heat flux exchange across the ocean–atmosphere interface. However, few long-term, high-resolution, and accurate sea–surface longwave radiation (SSLR) products are available. We generated the first high-resolution (5-km) all-sky daily SSLR product from Advanced Very High-Resolution Radiometer (AVHRR) top-of-atmosphere observations, combined with the European Center for Medium-Range Weather Forecasts Reanalysis V5 near-surface meteorological variables and National Oceanic and Atmospheric Administration sea–surface temperatures from 1981 to 2018. We coupled the densely connected convolutional neural network and bidirectional long short-term memory neural network as a retrieval algorithm. The training dataset was generated using integrated SSLR samples from 2002 to 2012 at 437 globally distributed locations. The archived product, SSLR_AVHRR, showed a high accuracy against 81,546 buoy-based observations from eight observation networks, with an R2 of 0.96 (1.00, 0.77), root mean square error of 10.27 (4.51, 9.27) Wm−2, and mean bias error of −1.30 (0.30, −0.72) Wm−2 for DLR (ULR, NLR) retrievals. Based on SSLR_AVHRR, the global DLR (ULR) flux exhibited a significantly (p-value < 0.05) increasing trend of 1.03 (1.08) Wm−2/decade during 1982–2018. The trend was 0.24 (0.34) Wm−2/decade during 1982–2000, which increased to 1.79 (1.45) Wm−2/decade during 2001–2018. This globally increasing trend was dominantly impacted by the significant increases in high latitude, particularly in the Arctic Ocean. Trend variations at low latitudes, which were more frequent than at middle and high latitudes because of the El Niño-Southern Oscillation, mitigated the increasing rates of global DLR and ULR after strong El Niño years, whereas the global NLR flux remained relatively stable throughout the study period. This method can be extended and applied to estimate other air-sea fluxes based on a unified estimating framework to help mitigate imbalanced energy and freshwater budgets at the air-sea interface to some degree.

Insights from the first winter weather observations near Mount Everest's summit

Insights from the first winter weather observations near Mount Everest's summit

Snowmelt estimation using an empirical radiation model

This study developed a novel approach for estimating snowmelt using an empirical radiation model, referred to as the Empirical Radiation Snowmelt Model, or ERSM. ERSM assumes that energy available to melt snow is equivalent to the sum of the shortwave radiation absorbed by the snowpack and the net longwave radiation at the snow surface. ERSM has two components. The first component is an empirically determined relationship between the observed daily snowmelt and the daily potential shortwave radiation. The longwave radiation component is implicitly estimated through its influence on the observed snowmelt. The daily potential shortwave radiation was found using two calculated parameters: the top-of-the-atmosphere shortwave radiation and the albedo of the snow surface. This first component simulates the average snowmelt that will occur for a given level of the potential shortwave radiation. The second component of ERSM estimates the difference between the average snowmelt simulated from the potential shortwave radiation (first component) and the actual snowmelt that occurred on any day. Observations from 20 SNOTEL11SNOTEL-U.S. Department of Agriculture, Natural Resources Conservation Service Snow Telemetry site. sites in the Truckee River Watershed were used to develop separate ERSM parameters for each site. ERSM was able to robustly predict snowmelt for all sites considered in this study. The error statistics vary with the magnitude of the observed daily snowmelt and the best results were found for magnitudes at which the majority of snowmelt occurs. A particularly valuable application of ERSM is its use to estimate the melt factor required by temperature index snowmelt models. ERSM can explain a large degree of the observed variance of the temperature index melt factor.

Applying a distributed mass-balance model to identify uncertainties in glaciological mass balance on Brewster Glacier, New Zealand

AbstractA distributed mass-balance model is used over a 10-year period for the re-analysis of a glaciological mass-balance time series obtained from Brewster Glacier, New Zealand. Mass-balance modelling reveals glaciological mass balance has been overestimated, with an average mass loss of −516 mm w.e. a−1 not captured by observations at the end of the ablation season, which represents 35% of the annual mass balance. While the average length of the accumulation season (199 days) remains longer than the ablation season (166 days), melting is not uncommon in the core part of the accumulation season, with 2–32% of total snowfall being melted. Refreezing of meltwater is also important, with 10% of surface and subsurface melt being refrozen in the present climate. Net radiation, driven primarily by net shortwave radiation, is the main contributor to melt energy, with melt variability mainly influenced by the turbulent heat fluxes, net longwave radiation and the heat flux from precipitation in the ablation season. Snowfalls in summer are an important moderator of melt, highlighting the critical role of the ice-albedo feedback and phase of precipitation on seasonal mass balance. A complete homogenisation of the long-term glaciological mass balance for Brewster Glacier is still required.

Climatic and Vegetation Response Patterns over South Africa during the 2010/2011 and 2015/2016 Strong ENSO Phases

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation.

Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data

Abstract. Accurate estimates of snow water equivalent (SWE) based on remote sensing have been elusive, particularly in mountain areas. However, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to performing track-to-area (TTA) transformations of SWE observations in California's upper Tuolumne River watershed using synthetic data. The synthetic SWE measurements are designed to mimic a potential future P-band Signals of Opportunity (P-SoOP) satellite mission with a (along-track) spatial resolution of about 500 m. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend these relationships to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide, SWE inferred on 1 April using two synthetic satellite tracks (∼4.5 % basin coverage) led to percent errors of basin-averaged SWE (PEBAS) of 24.5 %, 4.5 % and 6.3 % in an extremely dry water year (WY2015), a normal water year (WY2008) and an extraordinarily wet water year (WY2017), respectively. Assuming a 10 d overpass interval, percent errors of basin-averaged SWE during both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ a feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net long-wave radiation (NetLong). We find that a modest increase in the accuracy of SWE estimation occurs when more than two ground tracks are leveraged. The accuracy of 1 April SWE estimation is only modestly improved for track repeats more often than about 15 d.

Improvements in the degree-day model, incorporating forest influence, and taking China’s Tianshan Mountains as an example

Study regionKunes River Basin in the western Tianshan Mountains, China. Study focusThe study used atmospheric meteorological data such as temperature, radiation and snow melt rate for areas with different degrees of openness. The climatic parameters, snow melt process, and major environmental controls were studied. By analysing an improved degree-day model that simulated the forest subcanopy, a subcanopy snowmelt model was constructed. New hydrological insights for the regionThe net shortwave radiation of snowy subcanopy surfaces is lower than that of open sites, whilst the net longwave radiation is higher. This suggests that the influence of longwave radiation on the snowmelt process in the subcanopy is greater than that of shortwave radiation. Furthermore, due to the relatively high night-time temperatures in the subcanopy, the influence of the water recession process was significant. In this study, net longwave radiation and water recession were added to a traditional degree-day model. The simulation showed that the improved degree-day model responded well. The R2 of each station was > 0.88, the Ens was > 0.87, and the PBIAS was 0.63 %− 8.44 %, providing a simple and feasible method for the simulation of snowmelt runoff.

Unprecedented Arctic sea ice thickness loss and multiyear-ice volume export through Fram Strait during 2010–2011

The satellite-observed sea ice thickness (SIT) records from 2003 to 2020 identify an extreme SIT loss during 2010–2011. Ice thickness budget analysis demonstrates that the thickness loss was associated with an extraordinarily large multiyear ice (MYI) volume export through the Fram Strait during the season of sea ice advance. High cloudiness led to positive anomalies of net longwave radiation, and positive net surface energy flux anomalies supported enhanced sea ice melt from June to August. Due to the MYI loss, the Arctic sea ice became more sensitive to subsequent atmospheric anomalies. The reduced surface albedo triggering a positive ice-albedo amplifying feedback and contributed to the accelerating loss of ice thickness. These tightly coupled events highlight that the increasingly younger and thinner Arctic sea ice is becoming more vulnerable to external forcing and created the precondition for the rapid reduction in sea ice extent in 2012.

The impact of climate oscillations on the surface energy budget over the Greenland Ice Sheet in a changing climate

Abstract. Climate change is particularly strong in Greenland, primarily as a result of changes in the transport of heat and moisture from lower latitudes. The atmospheric structures involved influence the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS), and their patterns are largely explained by climate oscillations, which describe the internal climate variability. By using k-means clustering, we name the combination of the Greenland Blocking Index, the North Atlantic Oscillation index and the vertically integrated water vapor as NAG (North Atlantic influence on Greenland) with the optimal solution of three clusters (positive, neutral and negative phase). With the support of a polar-adapted regional climate model, typical climate features marked under certain NAG phases are inter-seasonally and regionally analyzed in order to assess the impact of large-scale systems from the North Atlantic on the surface energy budget (SEB) components over the GrIS. Given the pronounced summer mass loss in recent decades (1991–2020), we investigate spatio-temporal changes in SEB components within NAG phases in comparison to the reference period 1959–1990. We report significant atmospheric warming and moistening across all NAG phases. The pronounced atmospheric warming in conjunction with the increase in tropospheric water vapor enhance incoming longwave radiation and thus contribute to surface warming. Surface warming is most evident in winter, although its magnitude and spatial extent depend on the NAG phase. In summer, increases in net shortwave radiation are mainly connected to blocking systems (+ NAG), and their drivers are regionally different. In the southern part of Greenland, the atmosphere has become optically thinner due to the decrease in water vapor, thus allowing more incoming shortwave radiation to reach the surface. However, we find evidence that, in the southern regions, changes in net longwave radiation balance changes in net shortwave radiation, suggesting that the turbulent fluxes control the recent SEB changes. In contrast to South Greenland under + NAG, the moistening of North Greenland has contributed to decreases in surface albedo and has enhanced solar radiation absorption. Regardless of the NAG phase, increases in multiple atmospheric variables (e.g., integrated water vapor and net longwave radiation) are found across the northern parts of Greenland, suggesting that atmospheric drivers beyond heat and moisture originated from the North Atlantic. Especially in the northern ablation zone, sensible heat flux has significantly increased in summer due to larger vertical and horizontal temperature gradients combined with stronger near-surface winds. We attribute the near-surface wind intensification to the emerging open-water feedback, whereby surface pressure gradients between the ice/snow-covered surface and adjacent open seas are intensified.

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

Energy fluxes, mass balance, and climate sensitivity of the Sutri Dhaka Glacier in the western Himalaya

Various regional climatic factors influence glacier mass balance and thus control the water budget of the Himalayan rivers. However, the scarcity of observational data hinders a detailed understanding of the processes governing glacier mass balances in the Himalaya. Here we analyze the mass balance of the Sutri Dhaka Glacier, a debris-free glacier in the Chandra basin (western Himalaya) combining field observations and a physically based model to understand the drivers of mass balance variability. The modeled energy flux showed that net shortwave radiation contributed 56% to the total surface energy fluxes, followed by net longwave radiation (27%), sensible heat (8%), latent heat (5%), and ground heat flux (4%). However, over the ablation zone, inward fluxes account for most of the total heat flux, resulting in strong summertime melting. The model estimated glacier mass balance was −1.09 ± 0.31 and −0.62 ± 0.19 m w.e. during 2015/16 and 2016/17, which matches well with the in situ glaciological mass balance of −1.16 ± 0.33 and −0.67 ± 0.33 m w.e., respectively. A sensitivity analysis demonstrates that the mass balance of the glacier is affected by both air temperature (−0.21 m w.e. a−1 °C−1) and precipitation (0.19 m w.e. a−1 (10%)−1) changes. Our study suggests that, the mass balance of the Sutri Dhaka Glacier is less sensitive to changes in the partitioning of precipitation into snow and rain because the majority of precipitation falls as snow during the winter when the temperature is well below 0°C.

Assessment of Water Stress in Rice Fields Incorporating Environmental Parameters

Rice is considered as a major crop due to its demand globally. Pakistan is famous throughout the world to produce export quality rice which have healthy contribution in boosting the regional economy. Rice plant require plenty of water for its proper growth and development therefore, water conservation is significant to maintain water reserves for a sustainable future. The main objective of this study was to identify day-to-day availability of water in rice fields from Germination to Ripening (GTR) using Carnegie Ames Stanford Approach (CASA) model. CASA model incorporates real-time parameter e.g., temperature, pressure, extraterrestrial radiations, Leaf Area Index (LAI), vapor pressure and sunshine hours to compute net-shortwave radiations (Rns), net-longwave radiations (Rnl), net-radiations (Rn), actual incoming radiations (Rso), sensible heat flux (H), ground heat flux (Go) and finally the water stress (W). The averaged values of Rn, Rso, Rns, Rnl and H were computed as 206, 319, 178, 34 and 124 (wm-2) respectively for GTR. Total expected sunshine hours were 1584h but we could receive only 874 h during GTR due to “off and on” cloud activity. LAI and Go were observed in inverse relation to each other.

Mass- and Energy-Balance Modeling and Sublimation Losses on Dokriani Bamak and Chhota Shigri Glaciers in Himalaya Since 1979

Available surface energy balance (SEB) studies on the Himalayan glaciers generally investigate the melt-governing energy fluxes at a point-scale. Further, the annual glacier-wide mass balance (Ba) reconstructions have often been performed using temperature-index (T-index) models. In the present study, a mass- and energy-balance model is used to simulate the Ba on Dokriani Bamak Glacier (DBG, central Himalaya) and Chhota Shigri Glacier (CSG, western Himalaya) using the bias-corrected ERA5 data from 1979 to 2020. The model is calibrated using in-situ Ba and validated against available in-situ altitudinal and geodetic mass balances. DBG and CSG show mean Ba of −0.27 ± 0.32 and −0.31 ± 0.38 m w.e. a−1 (meter water equivalent per year), respectively, from 1979 to 2020. Glacier-wide net shortwave radiation dominates the SEB followed by longwave net radiation, latent heat flux, and sensible heat flux. The losses through sublimation are around 22% on DBG and 20% on CSG to the total ablation with a strong spatial and temporal variability. Modeled Ba is highly sensitive to snow albedo —with sensitivities of 0.29 and 0.37 m w.e. a−1 for 10% change in the calibrated value—on DBG and CSG, respectively. The sensitivity of the modeled mean Ba to 1°C change in air temperature and 10% change in precipitation, respectively is higher on DBG (−0.50 m w.e. a−1°C−1, 0.23 m w.e. a−1) than the CSG (−0.30 m w.e. a−1°C−1, 0.13 m w.e. a−1). This study provides insights into the regional variations in mass-wastage governing SEB fluxes at a glacier-wide scale, which is helpful for understanding the glacier–climate interactions in the Himalaya and stresses an inclusion of sublimation scheme in T-index models.

Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016: 2. The Impact of Assimilating Satellite‐Based Snow Cover and Freeze/Thaw Observations Into a Land Surface Model

AbstractThis second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA.

The sensitivity of parameterization schemes in thermodynamic modeling of the landfast sea ice in Prydz Bay, East Antarctica

Abstract Based on the measurements conducted over the landfast sea ice in Prydz Bay, East Antarctica during the sea-ice growth season in 2016, various parameterization schemes in the high-resolution thermodynamic snow/ice model HIGHTSI are evaluated. The parameterization scheme of turbulent fluxes produces the largest errors compared with the parameterization schemes for other surface heat fluxes. However, the sea-ice thickness simulation is most sensitive to the differences in upward longwave radiation at the surface. In addition, the sea-ice thickness simulation during the growth season is highly sensitive to the oceanic heat flux, and a new oceanic heat flux parameterization scheme based on the bulk method is proposed. The new parameterization scheme is tested in a second year, and it significantly improves the model performance relative to the standard configuration when compared against observations. Finally, the seasonal variation in the heat budget and its influence on the sea-ice thickness variation are analyzed. The net shortwave radiation, sensible heat flux and conductive heat flux (the net longwave radiation and latent heat flux) are found to be the surface heat sources (heat sinks) during the growth season. The larger conductive heat flux and the smaller oceanic heat flux can intensify the growth of sea ice.

A direct algorithm for estimating clear-sky surface longwave net radiation (SLNR) from MODIS imagery

ABSTRACT Surface longwave net radiation (SLNR) is the key component of the surface radiation budget. SLNR is estimated by using the conventional method, in which surface longwave downward radiation (SLDR) and surface longwave upward radiation (SLUR) are estimated separately, and then SLNR is calculated by subtracting SLUR from SLDR. However, uncertainties in SLDR and SLUR may accumulate and propagate into the calculated SLNR, affecting the SLNR estimate accuracy. This paper proposes a direct algorithm that links clear-sky SLNR with the top-of-atmosphere (TOA) radiances in MODIS channels 29, 31 and 32 and column water vapour (CWV). The multivariate regression (MVR) and extreme gradient boosting (×GBoost) were employed to establish the relationships sequentially with collocated ground measurements and satellite data for high-latitude, mid-latitude, and low-latitude areas. According to the training and validation results, the GBoost model performs slightly better than the MVR model, which indicates that weak nonlinearity exists between clear-sky SLNR and its predictors. The absolute biases of the XGBoost models are approximately 1.0 Wm−2, and the RMSEs of the XGBoost model are 17.77, 27.33, and 19.60 Wm−2 in high-latitude, mid-latitude, and low-latitude areas. These results indicate that nonlinear relationships exist between clear-sky SLNR and predictors, but this nonlinearity is not strong. The superiority of the direct algorithm over the conventional method was also demonstrated. In the future, we will collect more data to improve the performance of the proposed direct algorithm especially in high-latitude and mid-latitude areas.

The surface energy balance of Austre Lovénbreen, Svalbard, during the ablation period in 2014

The ability to simulate the surface energy balance is key to studying land–atmosphere interactions; however, it remains a weakness in Arctic polar sciences. Based on the analysis of meteorological data from 1 June to 30 September 2014 from an automatic weather station on the glacier Austre Lovénbreen, near Ny–Ålesund, Svalbard, we established a surface energy balance model to simulate surface melt. The results reveal that the net shortwave radiation accounts for 87% (39 W m–2) of the energy sources, and is controlled by cloud cover and surface albedo. The sensible heat equals 6 W m–2 and is a continuous energy source at the glacier surface. Net longwave radiation and latent heat account for 31% and 5% of heat sinks, respectively. The simulated summer mass balance equals –793 mm w.e., agreeing well with the observation by an ultrasonic ranger.

The Energy and Mass Balance of Peruvian Glaciers

AbstractPeruvian glaciers are important contributors to dry season runoff for agriculture and hydropower, but they are at risk of disappearing due to climate change. We applied a physically based, energy balance melt model at five on‐glacier sites within the Peruvian Cordilleras Blanca and Vilcanota. Net shortwave radiation dominates the energy balance, and despite this flux being higher in the dry season, melt rates are lower due to losses from net longwave radiation and the latent heat flux. The sensible heat flux is a relatively small contributor to melt energy. At three of the sites the wet season snowpack was discontinuous, forming and melting within a daily to weekly timescale, and resulting in highly variable melt rates closely related to precipitation dynamics. Cold air temperatures due to a strong La Niña year at Shallap Glacier (Cordillera Blanca) resulted in a continuous wet season snowpack, significantly reducing wet season ablation. Sublimation was most important at the highest site in the accumulation zone of the Quelccaya Ice Cap (Cordillera Vilcanota), accounting for 81% of ablation, compared to 2%–4% for the other sites. Air temperature and precipitation inputs were perturbed to investigate the climate sensitivity of the five glaciers. At the lower sites warmer air temperatures resulted in a switch from snowfall to rain, so that ablation was increased via the decrease in albedo and increase in net shortwave radiation. At the top of Quelccaya Ice Cap warming caused melting to replace sublimation so that ablation increased nonlinearly with air temperature.

Structure of the Western Tibetan Vortex inconsistent with a thermally-direct circulation

The Western Tibetan Vortex (WTV) is a large-scale circulation pattern identified from year-to-year circulation variability, which was used to understand the causal mechanisms for slowdown of the glacier melting over the western Tibetan Plateau (TP). A recent argument has suggested the WTV is the set of wind field anomalies resulting from variability in near-surface air temperatures over the western TP (above 1500 m), which, in turn, is likely driven by the surface net radiation. This study thereby evaluates the above putative thermal-direct mechanism. By conducting numerical sensitivity experiments using a global atmospheric circulation model, SAMIL, we find a WTV-like structure cannot be generated from a surface thermal forcing imposed on the western TP. A thermally-direct circulation generated by the surface or near surface heating is expect to cause upward motions and a baroclinic structure above it. In contrast, downward motions and a quasi-barotropic are observed in the vertical structure of the WTV. Besides, we find variability of the surface net radiation (sum of the surface shortwave and longwave net radiation) over the western TP can be traced back to the WTV variability based on ERA5 data. The anticyclonic (cyclonic) WTV reduces (increases) the cloudiness through the anomalous downward (upward) motions, causes more (less) input shortwave net radiation and thereby more (less) surface net radiations, resulting in the warmer (cooler) surface and near-surface air temperature over the western TP. The argument is constructive in encouraging examination of the radiative balance processes that complements previous studies.

Surface energy balance of the Sygyktinsky Glacier, south Eastern Siberia, during the ablation period and its sensitivity to meteorological fluctuations

The physically based melt of the low elevation Eastern Siberian glaciers is poorly understood due to the lack of direct micrometeorological studies. We used an automatic meteorological station to record the meteorological and energy characteristics of the Sygyktinsky Glacier, south Eastern Siberia (56.8° N, 117.4° E, 2,560 m a.s.l.), during two ablation seasons and computed the surface energy balance (SEB) for 30-min intervals. The glacier ablation was both modeled and measured by stakes and a thermistor cable. The net radiation (Rnet) was the main contributor (71–75 W m−2, 89–95%) to the SEB (79 W m−2, 100%), followed by sensible (2–4 W m−2, 3–5%) and latent (2–3 W m−2, 2–4%) heat fluxes. The net shortwave radiation was the main positive component of Rnet, while the net longwave radiation was weak and either negative (− 15 W m−2 in 2019) or positive (4 W m−2 in 2020). The small proportion of turbulent fluxes in the SEB is explained by the low wind speed (1.2 m s−1). The glacier ablation was found to be more sensitive to changes in shortwave radiation and wind speed, suggesting the need to consider the atmospheric conditions of the ablation period (summer snowfalls, cloudiness, wind speed) when analyzing long-term trends in glacial changes.