Future projections of temperature and precipitation for Antarctica

Antarctica directly impacts the lives of more than half of the world’s population living in the coastal regions. Therefore it is highly desirable to project its climate for the future. But it is a region where the climate models have large inter-modal variability and hence it raises questions about the robustness of the projections available. Therefore, we have examined 87 global models from three modeling consortia (CMIP5, CMIP6, and NEX-GDDP), characterized their fidelity, selected a set of 10 models (MM10) performing satisfactorily, and used them to make the future projection of precipitation and temperature, and assessed the contribution of precipitation towards sea-levels. For the historical period, the multi-model mean (MMM) of CMIP5 performed slightly better than CMIP6 and was worse for NEX-GDDP, with negligible surface temperature bias of approximately 0.5°C and a 17.5% and 19% biases in the mean precipitation noted in both CMIP consortia. These biases considerably reduced in MM10, with 21st century projections showing surface warming of approximately 5.1 - 5.3°C and precipitation increase approximately 44 - 50% against ERA-5 under high-emission scenarios in both CMIP consortia. This projected precipitation increase is much less than that projected using MMM in previous studies with almost the same level of warming, implying approximately 40.0 mm/year contribution of precipitation towards sea-level mitigation against approximately 65.0 mm/year.

Complex Biomineralization Pathways of the Belemnite Rostrum Cause Biased Paleotemperature Estimates

Paleotemperatures based on δ18O values derived from belemnites are usually “too cold” compared to other archives and paleoclimate models. This temperature bias represents a significant obstacle in paleoceanographic research. Here we show geochemical evidence that belemnite calcite fibers are composed of two distinct low-Mg calcite phases (CP1, CP2). Phase-specific in situ measurement of δ18O values revealed a systematic offset of up to 2‰ (~8 °C), showing a lead–lag signal between both phases in analyses spaced less than 25 µm apart and a total fluctuation of 3.9‰ (~16 °C) within a 2 cm × 2 cm portion of a Megateuthis (Middle Jurassic) rostrum. We explain this geochemical offset and the lead–lag signal for both phases by the complex biomineralization of the belemnite rostrum. The biologically controlled formation of CP1 is approximating isotope fractionation conditions with ambient seawater to be used for temperature calculation. In contrast, CP2 indicates characteristic non-isotope equilibrium with ambient seawater due to its formation via an amorphous Ca-Mg carbonate precursor at high solid-to-liquid ratio, i.e., limited amounts of water were available during its transformation to calcite, thus suggesting lower formation temperatures. CP2 occludes syn vivo the primary pore space left after formation of CP1. Our findings support paleobiological interpretations of belemnites as shelf-dwelling, pelagic predators and call for a reassessment of paleoceanographic reconstructions based on belemnite stable isotope data.

Comparison of Ventilated and Unventilated Air Temperature Measurements in Inland Dronning Maud Land on the East Antarctic Plateau

Surface temperature measurements with naturally ventilated (NV) sensors over the Antarctic Plateau are largely subject to systematic errors caused by solar radiative heating. Here we examined the radiative heating error in Dronning Maud Land on the East Antarctic Plateau using both the newly installed automatic weather stations (AWSs) at NDF and Relay Station and the existing AWSs at Relay Station and Dome Fuji. Two types of NV shields were used in these AWSs: a multiplate radiation shield and a simple cylinder-shaped shield. In austral summer, the temperature bias between the force-ventilated (FV) sensor and the NV sensor never reached zero because of continuous sunlight. The hourly mean temperature errors reached up to 8°C at noon on a sunny day with weak wind conditions. The errors increased linearly with increasing reflected shortwave radiation and decreased nonlinearly with increasing wind speed. These features were observed in both the multiplate and the cylinder-shaped shields. The magnitude of the errors of the multiplate shield was much larger than that of the cylinder-shaped shield. To quantify the radiative errors, we applied an existing correction model based on the regression approach and successfully reduced the errors by more than 70% after the correction. This indicates that we can use the corrected temperature data instead of quality controlled data, which removed warm bias during weak winds in inland Dronning Maud Land.

Response of climate change impact on streamflow: the case of the Upper Awash sub-basin, Ethiopia

This paper discusses the response of climate change impact on future streamflow availability in Upper Awash River basin, Ethiopia. The change of climate was built using the CORDEX, RCM daily precipitation, maximum and minimum temperature under RCP4.5 and 8.5 scenarios. The climate model was examined in the historical period 1996–2015 for its ability of capturing observed precipitation and temperature. Bias correction was performed on RCM temperature and precipitation to minimize the uncertainties that may occur from climate model projection. After the successful calibration and validation of the HBV hydrological model, streamflow was simulated for the periods of 2021–2040 and 2041–2060 and compared to streamflow of the baseline period 1996–2015 to investigate the changes. The results suggested that overall, following the precipitation increment, streamflow is expected to increase under both RCPs. The average monthly changes of streamflow are expected to increase by 40.1 and 29.4% under RCP4.5 and 16.9 and 18.5% under RCP8.5 scenarios for 2021–2040 and 2041–2060, respectively. Annual streamflow would increase by 28.5 and 23.95% under RCP4.5 and 8.5 respectively. The results of this work can help and inform the water resources planner and designer to frame an appropriate plan and management for the effective use of water in the future.

A Practical Validation of Uncooled Thermal Imagers for Small RPAS

Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R2 > 0.99) in the temperature range 5–40 °C as well as a small (<2 °C) temperature bias that is less than the stated accuracy of the cameras. Field validation was accomplished by imaging vegetation and concrete targets (outdoors and at night), that were instrumented with surface temperature sensors. Environmental parameters (air temperature, humidity, pressure and, wind and gusting) were measured for several hours prior to imaging data collection and found to either not be a factor, or were constant, during the ~30 min data collection period. In-field results from imagery at five heights between 10 m and 50 m show absolute temperature retrievals of the concrete and two vegetation sites were within the specifications of the cameras. The methodology has been developed with consideration of active RPAS operational requirements.

Comparative Analysis of Two Approaches for Correcting the Systematic Ocean Temperature Bias of CAS-ESM-C

Currently, several ocean data assimilation methods have been adopted to increase the performance of air–sea coupled models, but inconsistent adjustments between the sea temperature with other oceanic fields can be introduced. In the coupled model CAS-ESM-C, inconsistent adjustments for ocean currents commonly occur in the tropical western Pacific and the eastern Indian Ocean. To overcome this problem, a new ensemble-based bias correction approach—a simple modification of the Ensemble Optimal Interpolation (EnOI) approach for multi-variable into a direct approach for a single variable—is proposed to minimize the model biases. Compared with the EnOI approach, this new approach can effectively avoid inconsistent adjustments. Meanwhile, the comparisons suggest that inconsistent adjustment mainly results from the unreasonable correlations between temperature and ocean current in the background matrix. In addition, the ocean current can be directly corrected in the EnOI approach, which can additionally generate biases for the upper ocean. These induced ocean biases can produce unreasonable ocean heat sinking and heat storage in the tropical western Pacific. It will generate incorrect ocean heat transmission toward the east, further amplifying the inconsistency introduced through the tropical air–sea interaction process.

An Assessment of Reprocessed GPS/MET Observations Spanning 1995–1997

We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by CDAAC in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (AS-on) during 1995–1997. We compared the CDAAC data set to the MERRA-2 reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET, which is unaffected by the presence of encryption. We find excellent agreement between the more limited single frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and JPL data sets, agreement suggests that the CDAAC AS-on processing and the single frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9,579 profiles, and the new single-frequency data set, consisting of 4,729 profiles, yields a total number of 11,531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities.

The interaction between the nocturnal Amazonian low-level jet and convection in CESM

A nocturnal Amazonian low-level jet (ALLJ) was recently diagnosed using reanalysis data. This work assesses the ability of CESM1.2.2 to reproduce the jet and explores the mechanisms by which the ALLJ influences convection in the Amazon. The coupled CESM simulates the nocturnal ALLJ realistically, while CAM5 does not. A low-level cold air temperature bias in the eastern Amazon exists in CAM5, thus the ALLJ is weaker than observed. However, a cold SST bias over the equatorial North Atlantic in the coupled model offsets the cold air temperature bias, producing a realistic ALLJ. Climate models significantly underestimate March-April-May (MAM) precipitation over the eastern Amazon. We ran two sensitivity experiments using the coupled CESM by adding bottom-heavy diabatic heating at noon and midnight for 2.5 hours along the coastal Amazon during MAM to mimic the occurrence of shallow precipitating convection. When heating is added during the early afternoon, coastal convection deepens and the ALLJ transports moisture inland from the ocean, preconditioning the environment for deep convective development during the ensuing hours. The increased convection over the eastern Amazon also moderately alleviates the equatorial Atlantic westerly wind bias, leading to deepening of the east Atlantic thermocline in the following months and partially improving the simulated June-July-August (JJA) Atlantic cold tongue in the coupled model. When heating is added at night, coastal convection does not strengthen as much and the ALLJ transports less moisture. Improvements in the simulated Atlantic winds and SST are negligible. Therefore, diurnal circulations matter to the organization of convection and rain across the Amazon, with impacts over the equatorial Atlantic.

Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation

Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications.