Bias Temperature Research Articles

Improving the atmospheric dispersion forecasts over Washington, D.C. using UrbanNet observations: A study with HYSPLIT model

UrbanNet data have been used to enhance the predictions of the Weather Research and Forecasting (WRF) model through observational nudging to improve temperature and wind predictions. HYSPLIT was utilized to understand the impact of using the observationally nudged WRF fields in dispersion modeling. The meteorological observations collected from the National Weather Service monitoring stations located at two major airports in Washington metropolitan area were also assimilated into WRF modeling. The results showed that observational nudging successfully adjusted WRF wind fields towards the observations and significantly reduced the forecast temperature bias at nighttime. The comparison of HYSPLIT simulations with and without the enhancement of the WRF model using UrbanNet and airport data showed significant differences in the pattern and direction of the dispersion plume especially during the early morning hours. Furthermore, ingesting the data from the closer airport to the downtown area in WRF provided HYSPLIT simulations very similar to the ones using UrbanNet data. This provides strong evidence that local data are essential to adjust weather prediction models routinely used to drive dispersion models. There was also evidence of increased mixing height when using local data collected in the downtown, mainly resulting from the increased surface heating in the city.

On the Importance of Regime-Specific Evaluations for Numerical Weather Prediction Models as Demonstrated Using the High-Resolution Rapid Refresh (HRRR) Model

Abstract The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates (), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts.

Development of Indian summer monsoon precipitation biases in two seasonal forecasting systems and their response to large-scale drivers

Abstract. The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.

Performance of high-resolution MUR satellite sea surface temperature data as a proxy for near-surface in situ temperatures on neotropical reefs

As ocean temperatures increase, so does the need for accurate, regular, and widespread temperature data. This is especially important for researchers studying corals, which can be pushed over their bleaching thresholds by small, sustained changes in water temperature. Satellite data products offer longitudinal estimates of sea surface temperature (SST) taken at regular intervals without the ongoing costs and geospatial limits associated with underwater temperature loggers. However, previous work indicates satellite data are sometimes characterized by significant temperature biases, particularly in stratified coastal waters. Here, we compared the foundation sea surface temperature (fSST) estimated from a highly processed, gridded satellite data set (GHRSST MUR L4 Global SST) to in situ temperature (IST) data collected from coral reef habitats in the top 20 m of the water column at 19 sites in Panama. Comparisons between Pacific and Caribbean coasts and between upwelling and non-upwelling seasons provide a robust test of the utility of this product to estimate subsurface temperatures. The mean differences between fSST and IST show that MUR fSST accurately reflects temperatures at shallow (<9 m) depths on the Caribbean coast (mean bias ≤0.55°C ± standard error (SE) 0.01°C) and during the non-upwelling season on the Pacific coast (mean bias ≤0.26°C ± SE 0.03°C). MUR fSST poorly reflects IST during the Pacific upwelling season, especially at sites 12 m and deeper. In such cases, 66% of satellite estimates deviated from ISTs by >1°C. Our findings underscore how highly processed SST data products should be ground-truthed before being used as a substitute for IST, especially in upwelling areas.

Skilful multiweek predictions of tropical cyclone frequency in the Northern Hemisphere using ACCESS‐S2

AbstractThe skill of subseasonal (multiweek) forecasts of tropical‐cyclone (TC) occurrence over the Northern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS‐S2. ACCESS‐S2 shows a good representation of the spatial distribution of TCs in the Northern Hemisphere; however, TC track frequency is generally underpredicted in the western North Pacific to the east of the Philippines and in the eastern North Pacific. The reduced activity relative to observations could be due to a significant positive bias in 850–200‐hPa wind shear in both of these regions, as well as a significant negative sea‐surface temperature (SST) bias in the eastern North Pacific. Despite biases in climatological TC frequency, the observed change in TC track frequency across the Northern Hemisphere with the phase of the Madden–Julian Oscillation (MJO) is well captured by ACCESS‐S2. Changes in the large‐scale environment (e.g., precipitation, 600‐hPa relative humidity, 850‐hPa absolute vorticity and 850–200‐hPa wind shear) are also well represented, with the location and size of the anomalies comparable to ERA‐Interim, apart from SST which shows a different response during some phases. ACCESS‐S2 shows skill relative to climatology for multiweek predictions of TC occurrence out to week 5 in the western North Pacific, eastern North Pacific and North Atlantic; and out to week 2 for the North Indian Ocean. Assessment of real‐time forecasts for Typhoon Rai (December 2021) showed that ACCESS‐S2 provided good guidance of the development and potential landfall of a TC in the Philippines at four weeks lead time.

Improving land surface feedbacks to the atmosphere in convection-permitting climate simulations for Europe

We investigated positive temperature (warm) and negative precipitation (dry) biases in convection-permitting model (CPM) simulations for Europe (2.2 km grid spacing) that were considerably larger than in equivalent regional climate model (RCM) simulations (12 km grid spacing). We found that improvements in dry biases could be made by (1) using a more complex runoff scheme which takes into account topography and groundwater, (2) delaying the onset of water stress in vegetation to enhance transpiration, (3) changing the microphysics scheme to CASIM (Cloud AeroSol Interacting Microphysics) which also decreases heavy rainfall and increases light rainfall. Increasing soil moisture to the critical point can remove dry precipitation biases in southern Europe but not in northern areas, indicating that soil moisture limitation is a key contributor to precipitation biases in the south only. Instead, in the north, changing the cloud scheme of the model has more impact on precipitation biases. We found that the more intense and intermittent nature of rainfall in the CPM, which is more realistic leads to different canopy interception compared to the RCM. This can impact canopy evaporation, evapotranspiration and feed back on precipitation. Increasing rainfall storage in the canopy only leads to small improvements in warm biases, since it still fills rapidly with intense CPM rainfall, suggesting the need for an additional moisture store via improved groundwater modelling or surface pooling. Overall, this work highlights the challenge of correctly capturing land surface feedbacks in CPMs, which play an important role in future climate projections in some regions.

Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China

The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.

Irreversible entropy transport enhanced by fermionic superfluidity

The nature of particle and entropy flow between two superfluids is often understood in terms of reversible flow carried by an entropy-free, macroscopic wavefunction. While this wavefunction is responsible for many intriguing properties of superfluids and superconductors, its interplay with excitations in non-equilibrium situations is less understood. Here we observe large concurrent flows of both particles and entropy through a ballistic channel connecting two strongly interacting fermionic superfluids. Both currents respond nonlinearly to chemical potential and temperature biases. We find that the entropy transported per particle is much larger than the prediction of superfluid hydrodynamics in the linear regime and largely independent of changes in the channel’s geometry. By contrast, the timescales of advective and diffusive entropy transport vary significantly with the channel geometry. In our setting, superfluidity counterintuitively increases the speed of entropy transport. Moreover, we develop a phenomenological model describing the nonlinear dynamics within the framework of generalized gradient dynamics. Our approach for measuring entropy currents may help elucidate mechanisms of heat transfer in superfluids and superconducting devices.

A method for homogenization of complex daily mean temperature data: Application at Beijing Observatory (1915–2021) and trend analysis

AbstractA homogenized daily mean temperature series from 1915 to 2021 at Beijing Observatory (BO) has been developed. A thorough investigation of observation records and historical metadata was carried out to identify specific non‐climatic biases. The inhomogeneities were detected and adjusted with the optimally selected reference stations using a statistical reduction methodology. The results indicated three types of non‐climatic biases in temperature records: the first was the relocation of BO from suburban to urban, which caused positive shifts in temperature records, especially for wintertime; the second arose from different methods for calculating daily mean temperature, which caused different sign biases varying with months; and the last was caused by the transition from manual to automatic measurements with site‐specific biases. The corresponding adjustments due to three type biases ranged from −1.13 to 0.63, −0.29 to 0.23 and −0.13 to 0.00°C, respectively. The new homogenized annual mean temperature series showed a warming trend of 0.199°C/decade during 1915–2021. The trend was more pronounced in winter than in the other three seasons. The warming trend in the new series is greater than those in the previous homogenized series (0.136–0.177°C/decade), primarily due to the more effective adjustment associated with the site relocation, particularly for more urban locations.

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future.

Climatic impacts induced by winter wheat irrigation over North China simulated by the nonhydrostatic RegCM4.7

Quantification of the impact of winter wheat irrigation on the climate and the occurrence of extreme climatic events over North China is crucial for regional adaptation planning. Previous related studies mainly focused on the impact on surface processes; however, few focused on the effects of extreme events using high-resolution nonhydrostatic regional climate models. Here, the 9-km-resolution nonhydrostatic RegCM4.7 was coupled with a crop irrigation scheme and an updated winter wheat irrigation dataset to better simulate irrigation effects. Two experiments were conducted with and without winter wheat irrigation to isolate the effects of irrigation. Results showed that irrigation simulation reduces the model biases in temperature, precipitation, latent heat flux, soil moisture, sensitive heat flux, and top-layer soil moisture. Moreover, it also reduces the bias and increases the correlation with observations obtained in irrigated areas, especially in summer, indicating better representation of irrigation schemes. Winter wheat irrigation tends to cause substantial cooling of the local surface maximum, minimum, and mean air temperatures (by −1.68, −0.34, and −0.79 °C, respectively) over irrigated areas of North China, with the largest changes observed in relation to maximum temperature. Additionally, precipitation is found to increase during spring and summer, which is strongly related to water vapor transport in the lower levels of the atmosphere. Further analyses indicated that the number of annual mean hot days decrease (−13.9 d), whereas the number of both comfort days (+10.2 d) and rainy days (days with total precipitation greater than 1 mm: +6.6 d) increase over irrigated areas, demonstrating beneficial feedback to human perception and agriculture. Fortunately, although the heat wave risk increases (number of annual mean heat wave days: +5.8 d), the impact is limited to small areas within irrigated region. Additionally, no notable change was found in terms of heavy rainfall events and precipitation intensity, which might be an undereastimation caused by the less water use in model simulation. Although winter wheat irrigation does not have notable impact on the climate of the surrounding region, it is an important factor for the local-scale climate.

The noise of the charge density waves in quasi-1D NbSe3 nanowires — contributions of electrons and quantum condensate

Low-frequency electronic noise in charge-density-wave van der Waals materials has been an important characteristic, providing information about the material quality, phase transitions, and collective current transport. However, the noise sources and mechanisms have not been completely understood, particularly for the materials with a non-fully gapped Fermi surface where the electrical current includes components from individual electrons and the sliding charge-density wave. We investigated noise in nanowires of quasi-one-dimensional NbSe3, focusing on a temperature range near the Pearls transition TP1 ∼ 145 K. The data analysis allowed us to separate the noise produced by the individual conduction electrons and the quantum condensate of the charge density waves before and after the onset of sliding. The noise as a function of temperature and electric bias reveals several intriguing peaks. We explained the observed features by the depinning threshold field, the creep and sliding of the charge density waves, and the possible existence of the hidden phases. It was found that the charge density wave condensate is particularly noisy at the moment of depinning. The noise of the collective current reduces with the increasing bias voltage in contrast to the noise of the individual electrons. Our results shed light on the behavior of the charge density wave quantum condensate and demonstrate the potential of noise spectroscopy for investigating the properties of low-dimensional quantum materials.

The Kondo scaling for spin thermocurrent in strongly correlated electron systems

One of the most important feature in the Kondo physics is the universal scaling behavior. In this study, we analyze the transport behavior of the spin thermocurrent driven by a small temperature bias and under a weak magnetic field. We conclude that the spin thermocurrent exhibits a universal scaling behavior, similar to the spin susceptibility. The validity of our conclusion is also checked by using the numerically exact hierarchical equations of motion approach. From our fitting result of the scaling function, it is found that the Kondo temperature can be determined by the maximum of the spin thermocurrent at T/TK=0.383 .

Evaluation and Correction of PurpleAir Temperature and Relative Humidity Measurements

The PurpleAir PA-II sensor provides low-cost in situ measurements of meteorological variables including temperature and relative humidity (RH), as well as fine particulate matter (PM2.5) in real time. The sensors have been used in several studies investigating intracity differences in temperature and PM2.5. While the adoption and use of low-cost sensors has many benefits, care must be taken to ensure proper calibration and testing. This is true not only for PM2.5 measurements but also for temperature and RH given the synergistic health impacts from extreme heat and air pollution exposure. Here, we compare continuous temperature and RH measurements from a PA-II sensor to measurements from a Campbell Scientific 107 temperature probe and Vaisala HMP45C RH probe. All three instruments were co-located from December 2021 to June 2023 in Asheville, North Carolina. We found that the PA-II has an overall high temperature bias of 2.6 °C and root mean square error (RMSE) of 2.8 °C. Applying a linear regression correction reduces RMSE to 1.0 °C, while applying the constant 4.4 °C correction suggested by PurpleAir reduces RMSE to only 2.2 °C. Our PA-II RH measurements have a low bias of −17.4% and uncorrected RMSE of 18.5%. A linear regression correction improves the RH RMSE to 4.5%. Applying the constant 4% RH correction suggested by PurpleAir reduces RMSE to only 14.8%. We present new correction factors that differ from those suggested by PurpleAir, which overcorrect the high temperature bias and undercorrect the low RH bias. We also show that our correction factors improve estimates of dewpoint temperature (RMSE of 0.6 °C and 0.9 °C) compared to the corrections suggested by PurpleAir.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Ocean response to a century of observation-based freshwater forcing around Greenland in EC-Earth3

The acceleration of Greenland ice sheet melting over the past decades is raising concern regarding the impacts on ocean circulation in the North Atlantic and Arctic regions. Global climate models struggle to assess these impacts as they do not include a realistic amount of meltwater from the Greenland ice sheet. Using an extended observation-based dataset of runoff and solid ice discharge for the recent historical period (1920–2019), we force the EC-Earth3 climate model following the ensemble approach and protocol of a previous study using a different model. We observe a slight increase of the ensemble mean AMOC with a large spread in the response: 0.20±0.81\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.20\\pm 0.81$$\\end{document} Sv for the maximum AMOC at 45∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}N. We notice that members with a strong initial AMOC state (18 Sv) show a strengthening of the AMOC, while members with intermediate AMOC strength between 16 and 18 Sv are not affected by the freshwater forcing. Weaker initial AMOC members respond with a mean weakening of -0.21±0.58\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${-}\\,0.21\\pm 0.58$$\\end{document} Sv of the AMOC. The AMOC ensemble spread is reduced by half in the freshwater forced ensemble at the end of the experiment and the negative trend is stronger compared to historical simulations without the forcing. This suggests the possible ability of the freshwater to constrain AMOC variability on multi-decadal time-scales. Comparison of the two ten member ensembles with ORAS5 reanalysis shows a reduction of the surface temperature and salinity biases in the freshwater forced ensemble in key regions such as the North Atlantic subpolar gyre and the Beaufort Gyre relative to the historical simulations. Recent trends are also more aligned with reanalysis in those regions. Arctic Ocean Atlantic Water subsurface core temperature is also closer to reanalysis but still strongly biassed. Members with a high AMOC initial state display an unrealistic Atlantic water layer core depth.

Assessing Climate Change Effects on Winter Wheat Production in the 3H Plain: Insights from Bias-Corrected CMIP6 Projections

Climate change exerts significant impacts on regional agricultural production. This study assesses the implications of climate change on winter wheat yields in the Huang-Huai-Hai Plain (3H Plain), utilizing bias-corrected climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for mid-21st century (2041–2060) and late 21st century (2081–2100) periods under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5). These projections were incorporated into the decision support system for agrotechnology transfer (DSSAT) CERES-Wheat model to forecast potential alterations in winter wheat production. Initial findings reveal that uncorrected CMIP6 projections underestimated temperature and precipitation while overestimating solar radiation across the southern 3H Plain. Following bias correction through the equidistant cumulative distribution function (EDCDF) method, the regional average biases for temperature, precipitation, and solar radiation were reduced by 18.3%, 5.6%, and 30.7%, respectively. Under the SSP2–4.5 and SSP5–8.5 scenarios, mid-21st century simulations predicted a 13% increase in winter wheat yields. Late 21st century projections indicated yield increases of 11.3% and 3.6% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, with a notable 8.4% decrease in yields south of 36° N under the SSP5-8.5 scenario. The analysis of climate change factors and winter wheat yields in the 3H Plain under both scenarios identified precipitation as the key contributing factor to yield increases in the northern 3H Plain, while temperature limitations were the primary constraint on yields in the southern region. Consequently, adaptive strategies are essential to mitigate climate change impacts, with a particular focus on addressing the challenges posed by elevated temperature in the southern 3H Plain.

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Abstract. The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error in ERA5 is reduced to 1.88 °C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses.

Mid-infrared emission of germanium-tin films produced by sputtering

In this work, the synthesis of germanium-tin (GeSn) films by magnetron sputtering is reported. A fractional factorial experiment was conducted; the varied factors were temperature, power applied to Ge target by a radio-frequency source, and power applied to the Sn target by a direct-current source. Vibrational modes, film composition, morphology, deposition rate, dark conductivity, Raman spectra, and Fourier-transform infrared (FTIR) spectra of the GeSn films were analyzed. Mid-infrared spectrum of emission and absorption was obtained by FTIR electroluminescence (EL) spectroscopy in the range of 400–4000cm−1, thermal emission was monitored by a FLIR camera in the range of 8–14μm, and power emitted from the GeSn films was measured in the range of 2.7–5.3μm by means of a InAsSb photodetector; results obtained by these approaches at room temperature and low-field bias were consistent. Sample Ge0.71Sn0.29 exhibited Ge Raman crystalline fraction of 85%; from FTIR EL spectrum were observed peaks of absorption located at 1084, and 606 cm−1 that was attributed to Si−Si bonds from the c-Si substrate, as well as broadband attenuation of 2% in the range of 1500−3500cm−1, this behavior was associated to the self-heating of this sample at 90 °C. Sample Ge0.3Sn0.7 showed Raman crystalline fraction of 16 and 22% for Ge and Sn, respectively; from FTIR EL spectrum were observed peaks of emission located at 2.74 μm (0.452 eV) and 6.66 μm (0.186 eV), as well as broadband emission in the range of 600–2000 cm−1 attributed to Planck’s law at a temperature of 45 °C; these results were correlated to the proper content of Ge and Sn, fluctuating current, and polymorphic morphology of this sample.

Parameterizing Mesoscale Eddy Buoyancy Transport Over Sloping Topography

AbstractMost of the ocean's kinetic energy is contained within the mesoscale eddy field. Models that do not resolve these eddies tend to parameterize their impacts such that the parameterized transport of buoyancy and tracers reduces the large‐scale available potential energy and spreads tracers. However, the parameterizations used in the ocean components of current generation Earth System Models rely on an assumption of a flat ocean floor even though observations and high‐resolution modeling show that eddy transport is sensitive to the potential vorticity gradients associated with a sloping seafloor. We show that buoyancy transport coefficient diagnosed from idealized eddy‐resolving simulations is indeed reduced over both prograde and retrograde bottom slopes (topographic wave propagation along or against the mean flow, respectively) and that the reduction can be skilfully captured by a mixing length parameterization by introducing the topographic Rhines scale as a length scale. This modified “GM” parameterization enhances the strength of thermal wind currents over the slopes in coarse‐resolution, non‐eddying, simulations. We find that in realistic global coarse‐resolution simulations the impact of topography is most pronounced at high latitudes, enhancing the mean flow strength and reducing temperature and salinity biases. Reducing the buoyancy transport coefficient further with a mean‐flow dependent eddy efficiency factor, has notable effects also at lower latitudes and leads to reduction of global mean biases.