Observed Surface Air Temperature Research Articles

Quantifying exposure biases in early instrumental land surface air temperature observations

AbstractExposure biases are a pervasive non‐climatic change in land air temperature records which have been introduced as a result of changes in the way thermometers were sheltered from solar radiation and the elements over time. Exposure biases have not been widely accounted for in observational records, due to difficulties detecting/correcting the bias using traditional homogenisation techniques; therefore, exposure biases still contribute significant uncertainty to the early period in global temperature compilations. Here, an empirical approach to address the bias arising from the introduction of Stevenson‐type screens from the late‐19th century is presented. The approach consists of: (1) an empirical analysis of 54 parallel measurement series to identify the characteristics of the exposure bias in four exposure classes; (2) the development of bias‐estimation models based on an analysis of which variables influence the bias; and (3) the application of the models to an extended version of CRUTEM5 (CRUTEM5_ext), based on exposure metadata, to quantify and reduce the bias. Step one identified differences between the temperatures recorded in Stevenson screens and early exposures, which vary seasonally, diurnally, and with location and exposure class. The largest biases (in mean temperatures) were found in freestanding exposures (up to −0.78°C annually) and in summer, while the smallest biases were generally found in wall‐mounted exposures (near‐0°C annually) and in winter. Significant relationships between the bias and temperature, downward top of atmosphere and/or received shortwave downward solar radiation were found in each exposure class and led to the development of three regression‐based bias‐estimation models. Application of these models to 1,960 mid‐latitude stations in CRUTEM5_ext, resulted in small (≤0.016°C) positive adjustments to the Northern Hemisphere mid‐latitude mean before 1880, and larger (≤−0.1°C) negative adjustments to the Northern and Southern Hemisphere mid‐latitude means between 1882–1934 and 1856–1900, respectively. Larger adjustments were estimated regionally: up to −0.57°C annually and −0.79°C seasonally in individual grid cells.

Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization

AbstractThe impact of soil moisture initialization on the 2016 Northeast Asian heat wave was explored using the Land Information System (LIS) Weather Research and Forecasting (WRF) model. To obtain advanced soil moisture estimates, various meteorological variables from the Global Data Assimilation System analysis and Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement observation data were used as atmospheric forcings for the offline simulation of the Noah land surface model (Noah‐LSM). Based on the LIS, Soil Moisture Active Passive satellite were assimilated in the Noah LSM simulation. The assimilated soil moisture estimates revealed the drier land surface conditions over Northeast Asia compared with the product from the National Centers for Environmental Prediction Final Analysis (FNL) and were used as the initial condition of the WRF model. The WRF experiment initialized by the assimilated soil moisture product (LIS experiment) exhibited the observed surface air temperature (SAT) and 500 hPa geopotential height (500 GPH) over Northeast Asia compared with the result from the experiment initialized by the FNL (CTL experiment). At Week 1, the LIS experiment simulated warmer SAT than that from the CTL experiment, which was induced by the negative anomaly of latent heat flux over Mongolia. Then, 500 GPH became strong and spatially expanded in response to the thermal low induced by the warmer SAT, and the SAT was further increased during Weeks 2 and 3.

Long-term changes in surface air temperature over the Chinese mainland during 1901-2020

The magnitude of long-term surface climate warming over some regions, such as the Chinese mainland, is still uncertain due to the lack of observational data early in the 20th century. In this study, the monthly data series of the average, maximum, and minimum temperatures in the Chinese mainland during 1901-2020 were constructed based on the daily surface air temperature observations from 60 stations across the country, and the characteristics of the average, maximum, and minimum temperature, and diurnal temperature range (DTR) changes were analyzed. Results show that (1) regional average annual mean temperature in the Chinese mainland rose by 0.14°C per decade, maximum temperature rose by 0.07°C per decade, minimum temperature rose by 0.19°C per decade, and DTR decreased by 0.13°C per decade. All these trends are statistically significant (p < 0.01); (2) the largest annual mean maximum temperature increase occurred in spring, followed by winter and autumn/summer, and the largest annual mean minimum temperature increase was in winter and spring, followed by autumn and summer; (3) annual mean DTR decreased significantly at a rate of -0.08, -0.12, -0.12, and -0.13°C per decade (p < 0.01) in spring, summer, autumn, and winter, respectively; (4) the stations with drops in maximum temperature were mainly in Central China, southern North China, the southeastern coastal areas, and the middle and lower reaches of the Yangtze River, and the stations with significant increases in minimum temperature were located in North China, Northeast China, and Northwest China; (5) the areas with the fastest dropping DTR were mainly located in Northeast China and North China. The maximum and minimum temperature series for China based on climate anomalies are comparable to those based on other currently available datasets.

Evaluation of Three Air Temperature Reanalysis Datasets in the Alpine Region of the Qinghai–Tibet Plateau

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP.

Exceptional warming over the Barents area

In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20–40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea. We identify a statistically significant record-high annual warming of up to 2.7 °C per decade, with a maximum in autumn of up to 4.0 °C per decade. Our results are compared with the most recent global and Arctic regional reanalysis data sets, as well as remote sensing data records of sea ice concentration (SIC), sea surface temperature (SST) and high-resolution ice charts. The warming pattern is primarily consistent with reductions in sea ice cover and confirms the general spatial and temporal patterns represented by reanalyses. However, our findings suggest even a stronger rate of warming and SIC-SAT relation than was known in this region until now.

Intercomparison of Local Warming Trends of Shanghai and Hong Kong Based on 120-Year Temperature Observational Data

Using surface air temperature observations from 1901 to 2020, this study compared the warming trends of Shanghai and Hong Kong over a period of 120 years. The statistical results reveal the following: (1) The average temperatures of the two cities underwent fluctuating increases during the past 120 years, with linear warming rates of 0.23 °C/decade in Shanghai and 0.13 °C/decade in Hong Kong. (2) The fluctuation ranges of maximum temperature in the two cities were considerably higher than those of mean temperature. Moreover, in both cities, the annual mean maximum temperature decreased during a phase of more than a decade. The fluctuation ranges of minimum temperature were smaller, whereas the linear increases were higher than those for the mean temperature. (3) The diurnal temperature ranges (DTRs) of the two cities decreased; a certain phase of the decreases in DTR in the two cities was caused by decreases in the maximum temperature. (4) At a certain stage of urban development, owing to the shading effect of new high-rise buildings, the solar shortwave radiation reaching the Earth’s surface decreased, and anthropogenic heat generated by the energy consumption of buildings and urban human activities at that time was not sufficient to make up for the reduced shortwave radiation. This result may have led to the declines in the maximum temperature experienced by both cities. (5) Currently, the number of hot days and extremely hot days in the two cities has increased significantly compared with that a century ago, indicating that climate warming has an adverse impact on human settlements.

Influence of Climate Changes on Hydrothermal Regime of Dark Gray Podzolized Soil of Western Forest Steppe

Thermal properties of soil are important conditions that determine the nature and intensity of soil processes, but the features of the formation of thermal and water regimes of dark gray podzolized soil of the Ukrainian Forest Steppe are insufficiently studied. The purpose of the presented materials is to highlight the dynamics of the annual course of temperature and humidity of the air and dark gray podzolized soil to determine the peculiarities of the formation of its hydrothermal regime in modern agro-climatic conditions of the Forest Steppe zone. The study of soil temperature was carried out during 2008-2019 in the Rivne region using generally accepted standards and methods (DSTU ISO 11464-2007, DSTU ISO 11465-2001, DSTU B B.2.1-17:2009, DSTU B B.2.1-25:2009). Surface air temperature observations throughout the study period indicate an increase in average annual temperature, and five-year averages of the sum of effective air temperatures above 10°C show tends to increase. The study of the relationship between the temperature of the surface air layer and dark gray soil in the Rivne region confirmed that the correlation of these indicators is linear. Analyzing the dynamics of temperature indicators, it should be noted a significant increase in maximum soil temperatures, due to which there is an increase in average annual temperatures. Detailing of soil warming indicators according to the data of ten years showed that during 2008-2017 the soils warmed up much more than in the average during the whole period of meteorological observations. According to the accepted classification, the studied soils belong to the seasonally freezing type, moderately warm subtype and genus with medium heat supply. The results obtained during the study allow to justify changes in the hydrothermal conditions of dark gray podzolized soil and determine the needs for corrective measures in crop production to maintain sustainable yields and ensure enhanced fertility reproduction

Urban Heat Islands Significantly Reduced by COVID‐19 Lockdown

AbstractThe significant reduction in human activities during COVID‐19 lockdown is anticipated to substantially influence urban climates, especially urban heat islands (UHIs). However, the UHI variations during lockdown periods remain to be quantified. Based on the MODIS daily land surface temperature and the in‐situ surface air temperature observations, we reveal a substantial decline in both surface and canopy UHIs over 300‐plus megacities in China during lockdown periods compared with reference periods. The surface UHI intensity (UHII) is reduced by 0.25 (one S.D. = 0.22) K in the daytime and by 0.23 (0.20) K at night during lockdown periods. The reductions in canopy UHII reach 0.42 (one S.D. = 0.26) K in the daytime and 0.39 (0.29) K at night. These reductions are mainly due to the near‐unprecedented drop in human activities induced by strict lockdown measures. Our results provide an improved understanding of the urban climate variations during the global pandemic.

Dynamical Downscaling of Surface Air Temperature and Wind Field Variabilities over the Southeastern Levantine Basin, Mediterranean Sea

The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters.

A detectable urbanization effect in observed surface air temperature data series in Pyongyang region, Democratic People's Republic of Korea

A significant urbanization effect in the observed surface air temperature (SAT) data series have been found in developing regions. Here we analyzed the possible impact of urbanization on long-term SAT trends in the Pyongyang region of the Democratic People's Republic of Korea by using data of SAT, wind data and Landsat TM images. Results show that the annual mean urbanization effect in the SAT series was about 0.051 °C/decade for Pyongyang station, 0.031 °C/decade for stations of medium-size cities, and 0.019 °C/decade for stations of small cities and towns, over the period 1969–2019. The annual mean urbanization contributions to the overall warming ranged from 5.3% to 13.1% for different city stations during the study periods, with Pyongyang station witnessing the largest urbanization contribution. Seasonally, the urbanization effect was the largest during wintertime. Urbanization contributions to trend of extreme temperature events are more evident in cold weather indices than warm indices, which reached nearly one-quarter or one-fifth and even 30%. Compared to China's mainland and Japan, the urbanization effect on the regional SAT trend in the Pyongyang region is smaller, but it is detectable.

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

Non-Stationary Frequency Analysis and Uncertainty of Heat Wave Events

Due to global warming, there is an increasing concern regarding persistent and severe heat waves. The maximum daily surface air temperature observations show strong non-stationary features, and the increased intensity and persistence of heat wave events have been observed in many regions. The heat wave persistence day frequency (HPF) curve, which correlates the intensity of a heat wave persistence event for days with return periods, can be a useful tool to analyze the frequency of heat wave events. In this study, non-stationary HPF curves are developed to explain the trend in the increase of the surface air temperature due to climate change, and their uncertainty is analyzed. The non-stationary HPF model can be used in climate change adaptation management such as public health, public safety, and energy management.

Detection of type of trends in surface air temperature in China

Detection of the type of trend in temperature data, by distinguishing between deterministic and stochastic trends, has important implications for understanding climate change. The Unit root tests (URTs) have been widely used for detecting the type of trend, but they do not consider the possibility of fractional integration and its influences. In this study, we detected the type of trends in observed surface air temperature during 1960–2019 at 558 stations across China, by considering fractional integration. The whole period was divided into three sub-periods by two structural breakpoints (denoted as SBP1 and SBP2). The fractional differencing parameter d was estimated by the Local Whittle (LW) function, and then three URTs (namely PP, KPSS, and ZA) were used to detect the type of trends in the temperature data during different sub-periods. The results indicated that the de-seasoned monthly temperature (DMT) series were fractionally integrated and, thus, exhibited long-range dependence characteristics, which significantly influenced the estimation of the slope of trend and detection of the type of trend. Compared with the LW function, the ordinary least squares method yielded biased estimation of the slope of trend, as it could not eliminate the influence of long-range dependence of the DMT series. The DMT series with weak long-range dependence or anti-persistent characteristics during 1960–SBP1, SBP1–SBP2, and SBP2–2019 were accurately detected as deterministic trends. However, the DMT series with strong long-range dependence during SBP1–2019 and 1960–2019 were detected to have stochastic trends by the KPSS test. Following the results of the fractional integration and URTs together, temperature over short periods in China were detected to have deterministic trends, but those at long periods were detected to have a combination of long-range dependence and deterministic trends.

Improving Predictions of Surface Air Temperature Anomalies over Japan by the Selective Ensemble Mean Technique

AbstractThe selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

Analysis of 20th century surface air temperature using linear dynamical modes.

A Bayesian Linear Dynamical Mode (LDM) decomposition method is applied to isolate robust modes of climate variability in the observed surface air temperature (SAT) field. This decomposition finds the optimal number of internal modes characterized by their own time scales, which enter the cost function through a specific choice of prior probabilities. The forced climate response, with time dependence estimated from state-of-the-art climate-model simulations, is also incorporated in the present LDM decomposition and shown to increase its optimality from a Bayesian standpoint. On top of the forced signal, the decomposition identifies five distinct LDMs of internal climate variability. The first three modes exhibit multidecadal scales, while the remaining two modes are attributable to interannual-to-decadal variability associated with El Niño-Southern oscillation; all of these modes contribute to the secular climate signal-the so-called global stadium wave-missing in the climate-model simulations. One of the multidecadal LDMs is associated with Atlantic multidecadal oscillation. The two remaining slow modes have secular time scales and patterns exhibiting regional-to-global similarities to the forced-signal pattern. These patterns have a global scale and contribute significantly to SAT variability over the Southern and Pacific Oceans. In combination with low-frequency modulation of the fast LDMs, they explain the vast majority of the variability associated with interdecadal Pacific oscillation. The global teleconnectivity of the secular climate modes and their possible crucial role in shaping the forced climate response are the two key dynamical questions brought about by the present analysis.

Analysis of continentality and anomality of Svalbard climate according to observations of surface air temperature in the second half of the XX century

The article discusses the regional features of the Svalbard climate based on the data of regular meteorological observations obtained at Norwegian, Russian and Polish stations. Attention is paid to the spatial features of the multi-year regime of surface air temperature (SAT). Climatic norms (1961-1990, WMO recommendations) calculated from all meteorological stations, as well as long-term changes in the spatial gradients of the SAT and estimates of the range of annual fluctuations, revealed stations (area) with a “continental” and “marine” type of climate, as well as trends in spatial heterogeneities of the Svalbard climate. The rate of “modern” climate warming (1990-2016) was, on average, three times higher compared with the period after II World War (1948-2016). The continentality and abnormality of the climate are analyzed. Conclusions are formulated about a regular change in the spatial features of the climate (SAT) and the manifestation of its anomalous nature in different periods (1960-1970, 1988 and 2005-2014).

Satellite-based estimation of growing season framing dates, weather instability aspect

The paper describes experience of analysis of the high time resolution satellite image series aimed on detection of growing season framing dates. Satellite data use for growing season detection were discovered as observation gap filling technique in areas with sparse network of meteorological stations. We discovered the possibilities of growing season detection using annual series of 1-day Moderate Resolution Imaging Spectroradiometer (MODIS) composites instead of the ground-based monitoring of surface air temperature. Particularly, the study was aimed on comparison of growing season detection results based on ground and satellite observations. We attracted Google Earth Engine platform to process initial (Terra/MODIS) satellite data series, derived map series of Normalized Difference Water Index (NDWI) with a 1-day time resolution, produced annual graphs of the index in the reference points of meteorological stations, detected growing season framing dates at the reference points for the period of 2000–2017, and compared detected growing seasons with their estimations made upon the analysis of surface air temperature dynamics at the same meteorological stations. Involvement of satellite data gives the possibility to monitor growing seasons not only at the points of meteorological stations but through the whole studied area highlighting heterogeneity in spatial distribution in growing season dates and regional differences of growing season dynamics. Currently, we concluded weak quality of growing season detection based upon the 1-day NDWI series, due to the noise presented on annual graphs of NDWI. Mean observed accuracy of detection in comparison with the ground observations of surface air temperature was ~ 11.3 days, while median accuracy value was ~ 10.5.

Arctic Sea‐Ice Variability During the Instrumental Era

AbstractArctic sea‐ice extent (SIE) has declined drastically in recent decades, yet its evolution prior to the satellite era is highly uncertain. Studies using SIE observations find little variability prior to the 1970s; however, these reconstructions are based on limited data, especially prior to the 1950s. We use ensemble Kalman filter data assimilation of surface air temperature observations with Last Millennium climate model simulations to create a fully gridded Arctic sea‐ice concentration reconstruction from 1850 to 2018 and investigate the evolution of Arctic SIE during this period. We find a decline of ∼1.25×106 km2 during the early 20th‐century warming (1910–1940). The 25‐year trends during this period are ∼33–38% smaller than the satellite era (1979–2018) but almost twice as large as previous estimates. Additionally, we find that variability of SIE on decadal timescales prior to the satellite era is ∼40% greater than previously estimated.

A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations

An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. Observations of 12 reference stations in Jiangsu from 1961 to 2008 and of 14 regions in China from 2010 to 2014 were selected. The analysis of surface air temperature observations was performed in terms of the mean absolute error (MAE), root mean square error (RMSE), consistency indicator (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC). The results indicate that compared with the traditional IDW and SRT methods, the IKR method has a high error detection rate. Furthermore, the IKR method achieves better predictions and fitting in the single-station and multistation regression experiments in Jiangsu and in the national multistation regression prediction experiment.

Diurnal seismicity cycle linked to subsurface melting on an ice shelf

ABSTRACTSeismograms acquired on the McMurdo Ice Shelf, Antarctica, during an Austral summer melt season (November 2016–January 2017) reveal a diurnal cycle of seismicity, consisting of hundreds of thousands of small ice quakes limited to a 6–12 hour period during the evening, in an area where there is substantial subsurface melting. This cycle is explained by thermally induced bending and fracture of a frozen surface superimposed on a subsurface slush/water layer that is supported by solar radiation penetration and absorption. A simple, one-dimensional model of heat transfer driven by observed surface air temperature and shortwave absorption reproduces the presence and absence (as daily weather dictated) of the observed diurnal seismicity cycle. Seismic event statistics comparing event occurrence with amplitude suggest that the events are generated in a fractured medium featuring relatively low stresses, as is consistent with a frozen surface superimposed on subsurface slush. Waveforms of the icequakes are consistent with hydroacoustic phases at frequency $ {\bf \gt} \bf 75\,{\bf Hz}$ and flexural-gravity waves at frequency $ \bf {\bf \lt}25\,{\bf Hz}$. Our results suggest that seismic observation may prove useful in monitoring subsurface melting in a manner that complements other ground-based methods as well as remote sensing.