Bias Temperature Research Articles

Mobile ions entering the IGBT gate oxide - electrical detection and failure localization by lock-in thermography

Small mobile ions, especially sodium and potassium are known to have the ability to infiltrate and move within gate oxide layers of silicon MOS devices. The ion transport in silicon oxide is either a temperature-driven, isotropic diffusion process, or an electrical ion current in presence of an electric field. Therefore, the high temperature gate bias test with negative polarity provides ideal conditions to identify the presence of mobile ions in the gate oxide of IGBT and MOSFET. Mobile ions in sufficient concentration can severely affect the electric characteristics, such as the turn-on and turn-off behavior of the MOS channel, by creating a shift in the gate threshold voltage or excessive sub-threshold currents. This paper shows several examples of how mobile ions can get into the gate oxide, e.g., via cracks in top side metallization, or by corrosive attack of cap layers. All these different failure modes develop the same electrical failure signature after temperature and/or gate bias stress. During the investigations on mobile ions, a special lock-in thermography method was developed that allows to localize the defect sites in the semiconductor by taking advantage of exclusive excitation of MOS cells affected by mobile ion contamination. Finally, after defect localization, further analytical methods can be applied to identify the exact nature of the access path to the gate oxide. Based on the test and analytical routine described above, a special test method was developed that allows to evaluate the permeability of the semiconductor chip top side structures towards mobile ions and to check for weak spots and potential leakage paths.

The Effects of Wave-Induced Stokes Drift and Mixing Induced by Nonbreaking Surface Waves on the Ocean in a Climate System Ocean Model

Oceanic general circulation models (OGCMs) are important tools used to investigate mechanisms for ocean climate variability and predict the ocean change in the future. However, in most current ocean models, the impact of sea surface waves as one of the most significant dynamic processes in the upper ocean is absent. In this study, the Stokes drift and the vertical mixing induced by nonbreaking surface waves derived from the wave model (WAVEWATCH III) are incorporated into a Climate System Ocean Model, and their effects on an ocean climate simulation are analyzed. Numerical experiments show that both physical processes can improve the simulation of sea surface temperature (SST) and mixed layer depth (MLD) in the Southern Hemisphere. The introduction of Stokes drift effectively reduces the subsurface warm bias in the equatorial tropics, which is caused by the weakening of vertical mixing in the equatorial region. The nonbreaking surface wave mainly reduces the temperature bias in the Southern Ocean by enhancing mixing in the upper ocean. For the MLD, the Stokes drift mainly improves the simulation of the winter MLD, and the nonbreaking surface wave improves the summer MLD. For MLD south of 40° S in summer, the introduction of nonbreaking surface waves resulted in a reduction of 11.86 m in MLD bias and 7.8 m in root mean square errors (RMSEs), respectively. For winter subtropical MLD in the Southern Hemisphere, considering the Stokes drift, the MLD bias and RMSEs were reduced by 2.49 and 5.39 m, respectively. Adding these two physical processes simultaneously provides the best simulation performance for the structure of the upper layer. The introduction of sea surface waves effectively modulates the vertical mixing of the upper ocean and then improves the simulation of the MLD. Thus, sea surface waves are very important for ocean simulation, so we will further couple a sea waves model in the Chinese Academy of Sciences Earth System Model (CAS-ESM) as part of their default model component.

Impacts of the North Atlantic biases on the upper troposphere/lower stratosphere over the extratropical North Pacific

The winter upper troposphere/lower stratosphere temperature/vertical motion response over the extratropical North Pacific induced by North Atlantic changes is not well understood. Here, using robust diagnostic calculations conducted in a fully coupled high-resolution climate model, we correct the North Atlantic ocean circulation biases and show that during wintertime, the North Atlantic cold surface temperature biases lead to a warmer upper troposphere/lower stratosphere over the extratropical North Pacific. In the upper troposphere/lower stratosphere over the extratropical North Pacific, this winter warming temperature response is linked to the vertical motion response through a simple leading order thermodynamic relationship between changes in the horizontal advection and adiabatic heating. The upper troposphere/lower stratosphere vertical motion response, which is also associated with the North Atlantic induced Walker circulation response over the tropical North Pacific, can provide a rough estimation of the upper troposphere/lower stratosphere warming response over the extratropical North Pacific.

An evaluation of eight global ocean reanalyses for the Northeast U.S. Continental shelf

The Northeast U.S. continental Shelf (NES) extending from the Gulf of Maine to Cape Hatteras, is a dynamic region supporting some of the most commercially valuable fisheries in the world. This study aims to provide a systematic assessment of eight widely used, intermediate-to-high spatial resolution global ocean reanalysis products (CFSR, ECCO, ORAS, SODA, BRAN, GLORYS, GOFS3.0, and GOFS3.1) against available in situ and satellite ocean observations. In situ observations include water level from tide gauges, and temperature and salinity from various sources including shipboard hydrographic data, and moorings on the NES. Overall, the coarser resolution products exhibit limited skill in the coastal environment, with the high-resolution products better representing the temperature and salinity on the NES. Common biases are found in all reanalyses and in some regions within the NES; for example, biases in temperature and salinity are larger in the southern Mid-Atlantic Bight than in the rest of the NES. There is no single reanalysis that performs well across all parameters in all regions within the NES, but GLORYS and BRAN stand out for their superior performance across the largest number of metrics, outperforming other products in 22 and 25 of the 65 metrics examined, respectively. SODA is the top performer among the coarser resolution products (CFSR, ECCO, ORAS and SODA). The Gulf Stream and local bathymetry are critical factors leading to differences between the reanalyses. Conditions in summer are less well represented than in winter. In particular, the Mid-Atlantic Bight Cold Pool is not reproduced in four (CFSR, ECCO, ORAS, BRAN) of the eight reanalyses.

The Canadian Atmospheric Model version 5 (CanAM5.0.3)

Abstract. The Canadian Atmospheric Model version 5 (CanAM5) is the component of Canadian Earth System Model version 5 (CanESM5) which models atmospheric processes and coupling of the atmosphere with land and lake models. Described in this paper are the main features of CanAM5, with a focus on changes relative to the last major scientific version of the model (CanAM4). These changes are mostly related to improvements in radiative transfer, clouds, and aerosol parameterizations, as well as a major upgrade of the land surface and land carbon cycle models and addition of a small lake model. In addition to changes to parameterizations and models, changes in the adjustable parameters between CanAM4 and CanAM5 are documented. Finally, the mean climatology simulated by CanAM5 for the present day is evaluated against observations and compared with that simulated by CanAM4. Although many of the aspects of the simulated climate are similar between CanAM4 and CanAM5, there is a reduction in precipitation and temperature biases over the Amazonian basin, global cloud fraction biases, and solar and thermal cloud radiative effects, all of which are improvements relative to observations.

Diagnosing Hurricane Barry Track Errors and Evaluating Physics Scalability in the UFS Short-Range Weather Application

To assess the performance and scalability of the Unified Forecast System (UFS) Short-Range Weather (SRW) application, case studies are chosen to cover a wide variety of forecast applications. Here, model forecasts of Hurricane Barry (July 2019) are examined and analyzed. Several versions of the Global Forecast System (GFS) and Rapid Refresh Forecast System (RRFS) physics suites are run in the UFS-SRW at grid spacings of 25 km, 13 km, and 3 km. All model configurations produce significant track errors of up to 350 km at landfall. The track errors are investigated, and several commonalities are seen between model configurations. A westerly bias in the environmental steering flow surrounding the tropical cyclone (TC) is seen across forecasts, and this bias is coincident with a warm sea surface temperature (SST) bias and overactive convection on the eastern side of the forecasted TC. Positive feedback between the surface winds, latent heating, moisture, convection, and TC intensification is initiated by this SST bias. The asymmetric divergent flow induced by the excess convection results in all model TC tracks being diverted to the east as compared to the track derived from reanalysis. The large differences between runs using the same physics packages at different grid spacing suggest a deficiency in the scalability of these packages with respect to hurricane forecasting in vertical wind shear.

Comparative analysis of CMIP5 and CMIP6 in conjunction with the hydrological processes of reservoir catchment, Chhattisgarh, India

Study regionMahanadi reservoir project complex (MRP), Chhattisgarh, India Study focusThis study assesses and compares the performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models in modeling the hydrological regime. The study compared the performance of 13 CMIP6 GCMs in two Shared Socioeconomic Pathways (SSEPs) with that of 16 CMIP5 GCMs in two Representative Concentration Pathways (RCPs). Before being used to drive the Soil and Water Assessment Tool (SWAT) for streamflow prediction, the raw CMIP outputs were adjusted and downscaled using Bias Correction and Spatial Disaggregation methods (BCSD). In addition, the water deficits were also assessed between the mean monthly released by the optimized model and the mean monthly water demand. Results of this research projected the domestic water demand for future scenarios (2023–2099). New hydrological insights for the study regionThe results of this study revealed that the genetic algorithm (GA) outperforms the various optimization techniques for CMIP5, with deficits observed by the GA algorithm. In addition, CCCmaCanESM2 was found to be the most efficient among CMIP5 GCMs, whereas MPI-ESM1–2-HR was used for CMIP6 GCMs. Overall, the CMIP6 multi-model mean ensemble (MMME) outperformed the CMIP5 MMME in simulating streamflow over the study area at annual and seasonal timescales. CMIP6 MMME also reduced maximum and minimum temperature biases over the study region significantly. In overall conclusion, the CMIP6 ensemble offers a lower margin of uncertainty for future climate projections and better credibility for hydrological effect analysis. The results further indicate that satisfactory steam flows can be obtained from the SWAT model while slight underestimations can be seen for higher discharges. In addition, the projected domestic water demand based on future population and irrigation demand clearly showcases significant annual increases.

Exploring the ability of the variable-resolution Community Earth System Model to simulate cryospheric–hydrological variables in High Mountain Asia

Abstract. Earth system models (ESMs) can help to improve the understanding of climate-induced cryospheric–hydrological impacts in complex mountain regions, such as High Mountain Asia (HMA). Coarse ESM grids, however, have difficulties in representing cryospheric–hydrological processes that vary over short distances in complex mountainous environments. Variable-resolution (VR) ESMs can help to overcome these limitations through targeted grid refinement. This study investigates the ability of the VR Community Earth System Model (VR-CESM) to simulate cryospheric–hydrological variables such as the glacier surface mass balance (SMB) over HMA. To this end, a new VR grid is generated, with a regional grid refinement up to 7 km over HMA. Two coupled atmosphere–land simulations are run for the period 1979–1998. The second simulation is performed with an updated glacier cover dataset and includes snow and glacier model modifications. Comparisons are made to gridded outputs derived from a globally uniform 1∘ CESM grid, observation-, reanalysis-, and satellite-based datasets, and a glacier model forced by a regional climate model (RCM). Climatological biases are generally reduced compared to the coarse-resolution CESM grid, but the glacier SMB is too negative relative to observation-based glaciological and geodetic mass balances, as well as the RCM-forced glacier model output. In the second simulation, the SMB is improved but is still underestimated due to cloud cover and temperature biases, missing model physics, and incomplete land–atmosphere coupling. The outcomes suggest that VR-CESM could be a useful tool to simulate cryospheric–hydrological variables and to study climate change in mountainous environments, but further developments are needed to better simulate the SMB of mountain glaciers.

Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

AbstractMost climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region.

Low-power Relaxation Oscillator with Temperature-compensated Thyristor Decision Elements

This paper presents a low-power 140 kHz relaxation oscillator (ROSC) for low-frequency clock generators and timers. In voltage-mode ROSCs, unavoidable shunt current consumption results from voltage slewing at the integration capacitor. The proposed circuit employs CMOS thyristor-based decision elements which effectively reduce shunt currents by exploiting internal positive feedback. A complementary-to-absolute temperature (CTAT) current reference compensates for the frequency’s temperature sensitivity. In order to achieve high negative temperature coefficient with small area and power overhead, the circuit reuses parts of the positive-to-absolute temperature (PTAT) bias generation block. Moreover, a modified start-up circuit with 3 times faster oscillator power-on is presented. The 0.09 mm{}^2 oscillator consumes 6.5 nW/kHz at 1.5 V to 2.5 V supply if only the CTAT source is considered, resulting in a power consumption of 907.4 nW at 140 kHz. The measured temperature coefficient of -514.7 ppm/K in the range of −40 °C to 85 °C shows an improvement of 5.5 times compared to the uncompensated case. A supply sensitivity of 2.62 %/V, a frequency resolution of 2.67 kHz/step, and an average clock jitter of 6.02 ns are achieved. The oscillator is embedded in an ultra-low power system-on-chip for autonomous environmental sensing.

Data Assimilation of Range- and Depth-Averaged Sound Speed from Acoustic Tomography Measurements in Fram Strait

Abstract The 2010–12 Acoustic Technology for Observing the Interior of the Arctic Ocean (ACOBAR) experiment provided acoustic tomography data along three 167–301-km-long sections in Fram Strait between Greenland and Spitsbergen. Ocean sound speed data were assimilated into a regional numerical ocean model using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system. The resulting state estimate matched the assimilated sound speed time series; the root-mean-squared (RMS) error of the sound speed estimate (∼0.4 m s−1) is smaller than the uncertainty of the measurement (∼0.8 m s−1). Data assimilation improved modeled range- and depth-averaged ocean temperatures at the 78°50′N oceanographic mooring section in Fram Strait. The RMS error of the state estimate (0.21°C) is comparable to the uncertainty of the interpolated mooring section (0.23°C). Lack of depth information in the assimilated ocean sound speed measurements caused an increased temperature bias in the upper ocean (0–500 m). The correlations with the mooring section were not improved as short-term variations in the mooring measurements and the ocean state estimate do not always coincide in time. This is likely due to the small-scale eddying and nonlinearity of the ocean circulation in Fram Strait. Furthermore, the horizontal resolution of the state estimate (4.5 km) is eddy permitting, rather than eddy resolving. Thus, the state estimate cannot represent the full ocean dynamics of the region. This study is the first to demonstrate the usefulness of large-scale acoustic measurements for improving ocean state estimates at high latitudes. Significance Statement Acoustic tomography measurements allow one to observe ocean temperature in large ocean volumes under the Arctic sea ice by measuring sound speed, which is hard to synoptically observe by other methods. This study has established methods for assimilation of depth- and range-averaged ocean sound speed from an acoustic tomography experiment in Fram Strait. For the first time, a 2-yr time series of ocean sound from acoustic tomography has been assimilated into an ocean state estimate. The results highlight the use of ocean tomography in ice-covered regions to improve state estimates of ocean temperature.

Improvements and Evaluation of the FLake Model in Dagze Co, Central Tibetan Plateau

FLake has been one of the most extensively used lake models in many studies for lake thermal structure simulations. However, due to the scarcity of lake temperature observations, its applicability and performance on lakes over the Tibetan Plateau are still poorly investigated, especially in small- to medium-sized lakes. In this study, based on water profile observations in Dagze Co, a medium-sized lake on the central Tibetan Plateau, the sensitivity of lake thermal features to three key parameters in FLake was investigated. The performance of FLake in reproducing the lake thermal features was evaluated and improved by optimizing these key parameters. The results showed that the FLake model with default parameter settings can generally reproduce the thermal features of Dagze Co, but there are still significant deviations compared to observation. The sensitive experiments demonstrated that the thermal structure of the lake obviously responds to the change in the water extinction coefficient (Kd), friction velocity (u*), and ice albedo (αice). Based on previous studies and sensitive experiments, the three key parameters were set to the optimized value, which substantially improved the performance of FLake. The values of bias and RMSE of simulated lake surface water temperature decreased from 3.08 °C and 3.62 °C to 2.0 °C and 2.48 °C after parameter optimization. The integration of a simple salinity scheme further improved the ability of FLake to reproduce the observed thermal features of Dagze Co. These results will improve our understanding of thermal processes in lakes on the Tibetan Plateau, as well as the applicability of lake models.

Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models

The warm sea surface temperature (SST) bias in the Southern Ocean (SO) has persisted in several generations of Coupled Model Inter-comparison Project (CMIP) models, yet the origins of such a bias remain controversial. Using the latest CMIP6 models, here we find that the warm SST bias in the SO features a zonally oriented non-uniform pattern mainly located between the northern and southern fronts of the Antarctic Circumpolar Current. This common bias is not likely to be caused by the biases in the surface heat flux or the strength of the Atlantic meridional overturning circulation (AMOC) — the two previously suggested sources of the SO bias based on CMIP5 models. Instead, it is linked to the robust common warm bias in the Northern Atlantic deep ocean through the AMOC transport as an adiabatic process. Our findings indicate that remote oceanic biases that are dynamically connected to the SO should be taken into account to reduce the SO SST bias in climate models.

Modeling atmosphere–land interactions at a rainforest site – a case study using Amazon Tall Tower Observatory (ATTO) measurements and reanalysis data

Abstract. Modeling the interactions between atmosphere and soil at a forest site remains a challenging task. Using tower measurements from the Amazon Tall Tower Observatory (ATTO) in the rainforest, we evaluated the performance of the land surface model JSBACH, focusing especially on processes influenced by the forest canopy. As a first step, we analyzed whether high-resolution global reanalysis data sets are suitable to be used as land surface model forcing. Namely, we used data from the fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5) and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). Comparing 5 years of ATTO measurements to near-surface reanalysis data, we found a substantial underestimation of wind speeds by about 1 m s−1. ERA5 captures monthly mean temperatures quite well but overestimates annual mean precipitation by 30 %. Contrarily, MERRA-2 overestimates monthly mean temperatures in the dry season (August–October) by more than 1 K, while mean precipitation biases are small. To test how much the choice of reanalysis data set and the reanalysis biases affect the results of the land surface model, we performed spin-up and model runs using either ERA5 or MERRA-2 and with and without a bias correction for precipitation and wind speed and compared the results. The choice of reanalysis data set results in large differences of up to 1.3 K for soil temperatures and 20 % for soil water content, which are non-negligible, especially in the first weeks after spin-up. Correcting wind speed and precipitation biases also notably changes the land surface model results – especially in the dry season. Based on these results, we constructed an optimized forcing data set using bias-corrected ERA5 data for the spin-up period and ATTO measurements for a model run of 2 years and compared the results to observations to identify model shortcomings. Generally, the shape of the soil water profile is not reproduced correctly, which might be related to a lack of vertical variability of soil properties or of the root density. The model also shows a positive soil temperature bias and overestimates the penetration depth of the diurnal cycle. To tackle this issue, potential improvements can be made by improving the processes related to the storage and vertical transport of energy. For instance, incorporating a distinct canopy layer into the model could be a viable solution.

A generative adversarial network–based unified model integrating bias correction and downscaling for global SST

This paper proposes a global sea surface temperature (SST) bias correction and downscaling unifying model based on the generative adversarial network. The generator of the model uses a bias correction module to correct the numerical model forecasting results. Then it uses a reusable shared downscaling module to improve the resolution of the corrected data gradually. The discriminator of the model evaluates the quality of the bias correction and downscaling results as a criterion for adversarial training. And a physics-informed dynamics penalty term is included in the adversarial loss function to improve the performance of the model. Based on the 1°-resolution SST forecasting results of the GFDL SPEAR (Seamless System for Prediction and Earth System Research) model, the authors select the Remote Sensing System observations as the refined targets and carry out validation experiments for three typical events at different scales and regions (ENSO, Indian Ocean dipole, and oceanic heatwave events). The model reduces the forecasting error by about 90.3% while increasing the resolution to 0.0625°×0.0625°, breaking the limitation of the resolution of the observation data, and the structural similarity with the observation results is as high as 96.46%.摘要本文提出了一种基于生成对抗网络的全球海表面温度(sea surface temperature, SST)偏差订正及降尺度整合模型. 该模型的生成器使用偏差订正模块将数值模式预测结果进行校正, 再用可复用的共享降尺度模块将订正后的数据分辨率逐次提高. 该模型的判别器可鉴别偏差订正及降尺度结果的质量, 以此为标准进行对抗训练. 同时, 在对抗损失函数中含有物理引导的动力学惩罚项以提高模型的性能. 本研究基于分辨率为1°的GFDL SPEAR模式的SST预测结果, 选择遥感系统(Remote Sensing System)的观测资料作为真值, 面向月尺度ENSO与IOD事件以及天尺度海洋热浪事件开展了验证试验: 模型在将分辨率提高到0.0625°×0.0625°的同时将预测误差减少约90.3%, 突破了观测数据分辨率的限制, 且与观测结果的结构相似性高达96.46%.

In Situ VTOL Drone-Borne Observations of Temperature and Relative Humidity over Dome C, Antarctica

The Antarctic atmosphere is rapidly changing, but there are few observations available in the interior of the continent to quantify this change due to few ground stations and satellite measurements. The Concordia station is located on the East Antarctic Plateau (75° S, 123° E, 3233 m above mean sea level), one of the driest and coldest places on Earth. Several remote sensing instruments are available at the station to probe the atmosphere, together with operational meteorological sensors. In order to observe in situ clouds, temperature, relative humidity and supercooled liquid water (SLW) at a high vertical resolution, a new project based on the use of an unmanned aerial vehicle (drone) vertical take-off and landing from the DeltaQuad Company has been set up at Concordia. A standard Vaisala pressure, temperature and relative humidity sensor was installed aboard the drone coupled to an Anasphere SLW sensor. A total of thirteen flights were conducted from 24 December 2022 to 17 January 2023: nine technology flights and four science flights (on 2, 10, 11 and 13 January 2023). Drone-based temperature and relative humidity profiles were compared to (1) the balloon-borne meteorological observations at 12:00 UTC, (2) the ground-based microwave radiometer HAMSTRAD and (3) the outputs from the numerical weather prediction models ARPEGE and AROME. No SLW clouds were present during the period of observations. Despite technical issues with drone operation due to the harsh environments encountered (altitude, temperature and geomagnetic field), the drone-based observations were consistent with the balloon-borne observations of temperature and relative humidity. The radiometer showed a systematic negative bias in temperature of 2 °C, and the two models were, in the lowermost troposphere, systematically warmer (by 2–4 °C) and moister (by 10–30%) than the drone-based observations. Our study shows the great potential of a drone to probe the Antarctic atmosphere in situ at very high vertical resolution (a few meters).

Impacts of Oceanic Mesoscale Structures on Sea Surface Temperature in the Arabian Sea and Indian Summer Monsoon Revealed by Climate Model Simulations

Abstract Climate models typically have a cold sea surface temperature (SST) bias for the Arabian Sea, which regulates the Indian monsoon system as a water vapor source. Since the SST for the Arabian Sea is critical for the Indian monsoon, a better understanding of the processes that affect the SST is required. In this study, the effects of mesoscale oceanic variability on simulations of the Arabian Sea SST and Indian summer monsoon precipitation were investigated based on a comparison of climate model experiments in which non-eddying and eddy-permitting ocean components are coupled. In the eddy-permitting model, warm water advection driven by mesoscale variability near the Gulf of Aden and the Gulf of Oman increases the SST in the western Arabian Sea. Lateral eddy heat transport enhances warm water outflows to the Arabian Sea and suppresses surface water cooling by coastal upwelling during the southwestern monsoon season. Furthermore, a sensitivity experiment shows the primary importance of resolving oceanic mesoscale eddies and the secondary importance of resolving the Persian Gulf and Red Sea for the Arabian Sea SST. Also, the summer monsoonal precipitation decreases (increases) over the southeastern Arabian Sea (western and northern India) in the eddy-permitting model due to enhancement of wind convergence in the lower troposphere. Atmospheric general circulation model experiments indicate that the precipitation difference is partly caused by SST changes over the western Arabian Sea. The findings imply that the ocean resolution of climate models is a key factor in efficiently simulating the Indian monsoon.

Climatology of the Mount Brown South ice core site in East Antarctica: implications for the interpretation of a water isotope record

Abstract. Water stable isotope records from ice cores (δ18O and δD) are a critical tool for constraining long-term temperature variability at high latitudes. However, precipitation in Antarctica consists of semi-continuous small events and intermittent extreme events. In regions of high accumulation, this can bias ice core records towards recording the synoptic climate conditions present during extreme precipitation events. In this study we utilise a combination of ice core data, reanalysis products, and models to understand how precipitation intermittency impacts the temperature records preserved in an ice core from Mount Brown South in East Antarctica. Extreme precipitation events represent only the largest 10 % of all precipitation events, but they account for 52 % of the total annual snowfall at this site, leading to an overrepresentation of these events in the ice core record. Extreme precipitation events are associated with high-pressure systems in the mid-latitudes that cause increased transport of warm and moist air from the southern Indian Ocean to the ice core site. Warm temperatures associated with these events result in a +4.8 ∘C warm bias in the mean annual temperature when weighted by daily precipitation, and water isotopes in the Mount Brown South ice core are shown to be significantly correlated with local temperature when this precipitation-induced temperature bias is included. The Mount Brown South water isotope record spans more than 1000 years and will provide a valuable regional reconstruction of long-term temperature and hydroclimate variability in the data-sparse southern Indian Ocean region.

Using a SPATIAL INS/GNSS MEMS Unit to Detect Local Gravity Variations in Static and Mobile Experiments: First Results.

In this study, we present the feasibility of using gravity measurements made with a small inertial navigation system (INS) during in situ experiments, and also mounted on an unmanned aerial vehicle (UAV), to recover local gravity field variations. The INS operated is the SPATIAL one developed by Advanced Navigation, which has three-axis accelerometers. When the temperature bias is corrected, these types of INS are powerful enough to present the periodic signal corresponding to the solid Earth tides. There is also a clear correlation with the data measured at different altitudes by a CG5 gravimeter. However, these data were recorded on static points, so we also studied the INS in a moving platform on a UAV. Because there are a lot of vibrations recorded by the INS (wind, motor, on-board computer), the GPS and accelerometric data need to be filtered extensively. Once the data are corrected so they do not show thermal bias and low-pass filtered, we take the second derivative of the altitude (GPS) data to find the radial accelerometry of the drone and compare it to the radial accelerometry measured directly by the INS, in order to isolate the accelerometric signal that is related to the area that is being studied and the altitude. With a high enough precision, this method could be used to obtain the gravity variations due to the topography and density variations in the ground.

Evidence of Urban Blending in Homogenized Temperature Records in Japan and in the United States: Implications for the Reliability of Global Land Surface Air Temperature Data

Abstract To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed. Significance Statement Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.