Comment on tc-2021-388

The Arctic is experiencing amplified climate warming, decreasing sea ice extent, increasingly earlier springtime snowmelt, and a related increase in fire activity. The transition from cold to warm season in the Arctic strongly varies between years, but our understanding of temperature and surface energy budget changes over the springtime is limited. Here we investigate intraseasonal variability of Arctic springtime temperature extremes, the subsequent surface energy budget component response, and their interannual trends over 40 years (1981 – 2020) across the terrestrial Arctic (above 60° N) using ERA5-Land reanalysis data. We found the central and western Siberian regions to have the highest interannual variability in spring temperature anomaly among all Arctic regions during the 40-year period. Also in this region, we discovered the strength increased for heat extremes and decreased for cold extremes when comparing the first and the last 20 years of our study. Peaks in composited extreme temperature and surface energy budget anomalies were observed to occur concurrently, indicating temperature extremes are not driven by surface energy budget components. Lastly, by utilizing power spectrum analyses, we identified the primary driver of temperature anomaly interannual variability to be operating at a 1 to 2 week frequency. Based on our findings and observations in the recent literature, we hypothesize that the observed interannual variability in springtime temperature can be attributed to increased Arctic sea ice decline and an increase in the frequency and strength of associated atmospheric blocking events.

Net all-wave and short-wave radiation were recorded during a period of 21 months. Net long-wave radiation was determined as the difference between measured net all-wave and net short-wave radiation. The mean of daily radiation for each month, is presented. Net long-wave radiation was also calculated by two formulae based on Brunt's relation for effective emission. The formula given by Monteith [3] showed good agreement between observed and calculated net long-wave radiation.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21^st century, making it one of the largest contributors to future sea-level rise. The latest Climate Model Intercomparison Project 6th phase (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors such as melt-albedo feedbacks and cloud-related feedbacks will partly impact future melt and sea-level rise contribution, but little is yet known about the role of these regional factors in differences in GrIS surface melt projections between CMIP6 to CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Mod&eacute;le Atmosph&eacute;riqe R&eacute;gional (MAR) to physically downscale six CMIP5 RCP8.5 and five CMIP6 SSP5-8.5 extreme high-emission scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21^st century, but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is largest during autumn with a reduction of 14.1 &plusmn; 4.8 mmWE for a regional warming of +6.7 &deg;C, 12.5 mmWE more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn &ndash; in addition to enhanced cloud optical depth during autumn &ndash; to be the main drivers of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors; 1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures, 2) previously undocumented cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

[1] The surface energy budget and ablation were measured in the ablation zone of Parlung No. 4 Glacier on the southeast Tibetan Plateau (29°14′N, 96°55′E) during boreal summer 2009. The present study examines the summertime surface energy fluxes to identify major atmospheric variables governing the surface melt and their phenomenological links to the progression of the South Asian monsoon. Turbulent sensible heat and latent heat fluxes were calculated using the bulk aerodynamic approach, the accuracy of which was verified through a comparison with eddy-covariance flux measurements. The surface ablation calculated by the energy balance model was also verified by measurements of ablation stakes. Our results found the following percentage contributions to the total melt energy: net shortwave radiation, 98%; net longwave radiation, −12%; sensible heat, 16%; latent heat, −1%; and subsurface fluxes, −1%. The combined roles of cloud cover and surface albedo appear to control the surface energy balance during the onset period of the South Asian monsoon. The cloud variations affect surface melting with the advancement of the monsoon. Intensification of the South Asian monsoon probably accelerates melting in the ablation zone, whereas weakening of the monsoon reduces glacier melting, mainly because of changes in downward longwave irradiance and heat release due to vapor condensation. Moreover, the temperature index model proves useful for long-term mass balance and ablation modeling in cases where the degree-day factors are calibrated. But incorporating incoming shortwave radiation into the model should be more applicable and practical in this region.

We compare measurements of the turbulent and radiative surface energy fluxes from an automatic weather station (AWS) on Larsen C Ice Shelf, Antarctica with corresponding fluxes from three high‐resolution atmospheric models over a 1 month period during austral summer. All three models produce a reasonable simulation of the (relatively small) turbulent energy fluxes at the AWS site. However, biases in the modeled radiative fluxes, which dominate the surface energy budget, are significant. There is a significant positive bias in net shortwave radiation in all three models, together with a corresponding negative bias in net longwave radiation. In two of the models, the longwave bias only partially offsets the positive shortwave bias, leading to an excessive amount of energy available for heating and melting the surface, while, in the third, the negative longwave bias exceeds the positive shortwave bias, leading to a deficiency in calculated surface melt. Biases in shortwave and longwave radiation are anticorrelated, suggesting that they both result from the models simulating too little cloud (or clouds that are too optically thin). We conclude that, while these models may be able to provide some useful information on surface energy fluxes, absolute values of modeled melt rate are significantly biased and should be used with caution. Efforts to improve model simulation of melt should initially focus on the radiative fluxes and, in particular, on the simulation of the clouds that control these fluxes.

To identify the atmospheric controls of the summertime glacier surface energy balance in the Himalayas, in situ meteorological data collected at 6,523 m above sea level during May–July 2005 were obtained and analyzed. Our results showed that net shortwave radiation (103 W m−2) and turbulent sensible heat flux (12 W m−2) acted as energy sources, and net longwave radiation (−62 W m−2) and turbulent latent heat flux (−20 W m−2) represented heat sinks. Cloud cover controlled the summer surface energy balance. During the active period of the South Asian Summer Monsoon, the frequent cloud coverage increased the incoming longwave radiation more than it decreased the incident solar radiation. Intensification (weakening) of the South Asian Summer Monsoon strengthened (suppressed) surface melting. The melt energy measured during the nonmonsoon period was small due to the energy consumption associated with glacier volume warming, energy loss from sublimation, and large heat loss through net longwave radiation due to the low amount of incoming longwave radiation caused by the low cloudiness. The comparison of glacier surface energy balances on the Tibetan Plateau shows that on continental glaciers, net radiation is lower and accounts for a smaller contribution to energy sources, and the dominant energy sinks are sublimation and evaporation, rather than melting, which is the primary energy sink for maritime/subcontinental glaciers. This implies an important spatial variability in glacial sensitivity to different climatic conditions on the Tibetan Plateau.

A 5 year record of data from an automatic weather station (AWS) operating in the ablation zone of Storbreen, Norway, has been used to calculate the local surface energy and mass balance. The AWS observations cover five mass-balance years with an unusually strong mass deficit on Storbreen. The average energy flux (Q) contributing to melt for the period 2001–06 is 113 W m−2. Of this, the net shortwave radiation flux is the dominant contributor (92 W m−2), followed by the sensible heat flux (20 W m−2) and the latent heat flux (9 W m−2). The net longwave radiation (–6 W m−2) and the subsurface heat flux (–2 W m−2) contribute negatively to the budget. Net radiation thus produces 76% of the melt, while the turbulent fluxes and the subsurface heat flux produce 24% of the total melt. The seasonal mean incoming shortwave radiation is remarkably constant between the years, whereas variations in temperature and reflected shortwave radiation (albedo) explain most of the interannual variation in melt. The modelled ablation compares well with the measured ablation from stake readings. The sensitivity of the energy-balance model was examined by varying the surface roughness length of momentum and the sensitivity of the calculated melt by perturbations of temperature, wind speed and relative humidity.

The radiation balance of an apple tree

An algorithm for retrieval of surface net radiation with Landsat TM/ETM+ data was developed in two parts in this study. First of all, net short wave radiation was computed with an improved algorithm for atmospheric correction based on dark object scheme associated with topographic information of the target site to account for the topographic affect on radiant regimes, and then a thermal radiant transfer model for accurate estimation of net long wave radiation over mountainous region by using surface temperature derived from the extended mono-window algorithm to integrate with surface energy balance equations was established. The thermal radiation from surrounding surface thus can be estimated, and atmospheric downward long wave radiation was calculated by means of an empirical formula related to surface temperature. With these two terms, Pixel based surface net radiation can be obtained. An evident advantage of this model is that only two atmospheric parameters, i.e. atmospheric mean effective temperature and surface temperature, are needed.The proposed approach was experimentally applied to a mountainous region located in the Hanjiang Basin, south western China. 6S code was utilized to the Landsat ETM+ image acquired over the region to simulate the surface and atmospheric radiative parameters under different geographic zones at selected climatic conditions to examine the performance of the proposed model. It was suggested that the maximum error in albedo extraction by the proposed algorithm is about 17% with mean error less than 10%, the maximum error in retrieved short wave net radiation and long wave net radiation is about 15% and 12% with mean error less than about 11% and 9% respectively, which suggested the proposed approach an valid and operational mean in quantitative reversion of surface long wave radiation with satisfied accuracy.

Abstract. During recent summers (2007–2012), several surface melt records were broken over the Greenland Ice Sheet (GrIS). The extreme summer melt resulted in part from a persistent negative phase of the North Atlantic Oscillation (NAO), favoring warmer atmospheric conditions than normal over the GrIS. Simultaneously, large anomalies in sea ice cover (SIC) and sea surface temperature (SST) were observed in the North Atlantic, suggesting a possible connection. To assess the direct impact of 2007–2012 SIC and SST anomalies on GrIS surface mass balance (SMB), a set of sensitivity experiments was carried out with the regional climate model MAR forced by ERA-Interim. These simulations suggest that perturbations in SST and SIC in the seas surrounding Greenland do not considerably impact GrIS SMB, as a result of the katabatic wind blocking effect. These offshore-directed winds prevent oceanic near-surface air, influenced by SIC and SST anomalies, from penetrating far inland. Therefore, the ice sheet SMB response is restricted to coastal regions, where katabatic winds cease. A topic for further investigation is how anomalies in SIC and SST might have indirectly affected the surface melt by changing the general circulation in the North Atlantic region, hence favoring more frequent warm air advection towards the GrIS.

The Greenland Ice Sheet (GrIS) has been losing mass at an accelerating rate since the mid-1990s. This has been due to both increased ice discharge into the ocean and melting at the surface, with the latter being the dominant contribution. This change in state has been attributed to rising temperatures and a decrease in surface albedo. We show, using satellite data and climate model output, that the abrupt reduction in surface mass balance since about 1995 can be attributed largely to a coincident trend of decreasing summer cloud cover enhancing the melt-albedo feedback. Satellite observations show that, from 1995 to 2009, summer cloud cover decreased by 0.9 ± 0.3% per year. Model output indicates that the GrIS summer melt increases by 27 ± 13 gigatons (Gt) per percent reduction in summer cloud cover, principally because of the impact of increased shortwave radiation over the low albedo ablation zone. The observed reduction in cloud cover is strongly correlated with a state shift in the North Atlantic Oscillation promoting anticyclonic conditions in summer and suggests that the enhanced surface mass loss from the GrIS is driven by synoptic-scale changes in Arctic-wide atmospheric circulation.

Über den einfluss einer strohdecke auf den strahlungshaushalt des erdbodens

A 15‐month meteorological data set was obtained from an automatic weather station over a blue ice area in Dronning Maud Land, Antarctica. The meteorological measurements are used as input for a surface energy balance model in order to compute the hourly varying surface fluxes and the (sub)surface temperatures to a depth of 10 m. The model reproduces reasonably well the directly measured temperatures in the upper meter of ice. Model results show that the net shortwave radiation is the largest positive term in the annual mean energy budget (42.2 W m−2). Other positive fluxes are the downward sensible heat flux (12.1 W m−2) and the upward subsurface energy flux (0.2 W m−2). Energy is lost by net longwave radiation (−49.1 W m−2) and the upward directed latent heat flux (−5.0 W m−2). We analyze the annual cycle of the surface heat fluxes on the basis of daily, monthly, and seasonally mean values. In addition, we calculate the surface energy budget for two distinctly different weather regimes, which are typical for this region. Finally, we demonstrate that the annual cycle of the turbulent fluxes can be explained in terms of the limiting values of the Bowen ratio.

Surface longwave net radiation (LWNR) is a vital component in the surface radiation budget. Major progress has been made in the estimations of clear-sky LWNR. However, the estimation of cloudy-sky LWNR remains a significant challenge. In this paper, a linear model (LM) and a multivariate adaptive regression spline (MARS) model were developed to estimate the cloudy-sky LWNR from a satellite-derived surface shortwave net radiation product. Spatially and temporally matched satellite data and ground-measured LWNR, which was collected at 24 sites from four networks, were used to build and validate the linear and MARS models. The effects of land cover, climate type, and surface elevation on the estimate of LWNR were also analyzed. The MARS model, incorporating the normalized difference vegetation index (NDVI) and surface elevation (H) as the inputs, had the best performance. The determination coefficient, BIAS, and root mean square error (RMSE) were 0.51, 0.01 W/m2, and 26.10 W/m2, respectively. The developed model, when combined with freely distributed Global LAnd Surface Satellite (GLASS) products, showed promise for producing surface LWNR and all-sky surface net radiation.

&amp;lt;p&amp;gt;The North Atlantic Oscillation (NAO) index describes the hemispheric meridional oscillation of atmospheric masses above near Iceland and the subtropical Atlantic regions. In specific, NAO index indicates the differences in atmospheric pressure patterns between the two regions. Strong positive NAO index values relate to significant pressure differences, exposing US East and Northern Europe to warmer weather conditions, and Southern Europe to colder weather conditions. Negative NAO index values indicate weaker pressure differences, exposing US East and Northern Europe to cold weather, and Southern Europe to warm weather. A significant portion of the Atlantic sector climate is associated with NAO index and its variability. Historically, NAO index values were in a positive trend between 1970s and 1980s. Highest positive values were reported in the early 1990s. By that time, it was suggested that NAO index positive trends contributed significantly to the global warming signal. Most recently, research outputs from climate model predictions suggest that NAO index values will be more at the positive phase as a result of strong global warming signal. In a warmer climate the overall number of storms is predicted to decrease but storms will be more intense. However, more research is needed to understand variability trends in NAO index and to what extent they might be attributed to climate change impacts. Hence referred in here as NAO natural and non-natural, or else, climate change related variability trends. This study investigated the NAO variability trends by use of Singular Spectrum Analysis (SSA) and SSA based Entropy index. SSA is a statistical mechanics tool used to study the non-linear behavioral characteristics in complex geophysical, meteorological and climatic systems, monitored by time series data. The main objective of this analysis is to reveal the evolution of the NAO index dynamical system and convey information about the changing dynamics of the system. By use of SSA Entropy based index, the chaotic behavior of NAO index is studied. An SSA entropy based chaotic descriptor might entail information of the non-natural variability trend for NAO index values. Also the same descriptor might prove capable of defining a historical milestone of when this NAO variability trend started changing in an unpredictable- non natural- way owed to climate change forcing factors. NAO Index data are extracted from Climatic Research Unit, University of East Anglia from 1979-2018. SSA Phase space reconstruction by method of delays has been applied first to characterize the statistical and chaotic behavior of NAO patterns, by calculating variability and inconsistency descriptors. Phase space reconstruction allows analyzing time series data within the dynamics systems theory context. Following that, reconstructed attractor from the NAO observed time series allowed to build an approximation of the unknown observed states. Results revealed a highly variable and inconsistent behavior in NAO patterns over time. SSA Entropy based index investigation is currently underway to further understand the nature of inconsistency revealed in NAO patterns. Further research is expected to establish wind and wave storm patterns connections with NAO index patterns, through transfer dynamics concepts.&amp;lt;/p&amp;gt;