Bias Temperature Research Articles

Asymmetry and nonlinearity of current-bias characteristics in superfluid–normal-state junctions of weakly interacting Bose gases

We uncover current-bias characteristics of superfluid-normal state junctions with weakly-interacting Bose gases. It is shown that in the presence of a chemical potential bias the characteristics can strongly be asymmetric for origin. The salient feature that is absent in the fermionic counterpart arises from a tunneling process associated with a condensate and a bosonic Andreev reflection process. It turns out that such processes are intrinsically nonlinear and therefore do not obey Ohm's law even at a low bias. In addition, the remaining processes are found to obey Ohm's law and become dominant for transport driven by a temperature bias.

Challenges in modeling the energy balance and melt in the percolation zone of the Greenland ice sheet

AbstractIncreased surface melt in the percolation zone of the Greenland ice sheet causes significant changes in the firn structure, directly affecting the amount and timing of meltwater runoff. Here we force an energy-balance model with automatic weather stations data at two sites in the percolation zone of southwest Greenland ($2040$ and 2360 m a.s.l.) between spring $2017$ and fall $2019$. Extensive model validation and sensitivity analysis reveal that the skin layer formulation used to compute the surface temperature by closing the energy balance leads to a consistent overestimation of melt by more than a factor of two or three depending on the site. In contrast, model results match the observations well when the model is forced by observed surface temperatures; however, unexplained residuals in the energy balance occur. The sensible and ground heat flux differ markedly in the two simulations accounting largely for the difference in modeled melt amounts. This indicates that the energy available for melt is highly sensitive to small changes in surface temperature. Thus, regional climate models that also use the skin layer formulation may have a bias in surface temperature and melt energy in the percolation zone of the ice sheet.

Mean State of the Northern Hemisphere Stratospheric Polar Vortex in Three Generations of CMIP Models

Abstract Progress and persistent biases in the simulation of the stratospheric polar vortex from three generations of Coupled Model Intercomparison Projects (CMIPs) are assessed. On average, the stratospheric cold bias is largest in CMIP3, but is improved in CMIP5 and CMIP6. The climatological ridge in the stratosphere over the North Pacific is underestimated persistently across CMIPs. Four parameters of the stratospheric polar vortex are evaluated among models and among CMIPs, and some common biases are identified for most models, including the too-large size of the vortex periphery, overstrong strength, too-small aspect ratio of the vortex shape, and too-westward displaced a vortex centroid. Intermodel spread in some parameters is highly correlated with the sea surface temperature (SST) bias in northern tropical oceans. Namely, a cold SST bias in the northern tropical Pacific is associated with a too-strong and too-large polar vortex, and a cold SST bias in the northern tropical Atlantic seems to be related to the westward bias of the vortex centroid. The implications of biases in the mean state of the vortex for stratospheric events such as sudden stratospheric warmings (SSWs) are also identified in this study. Models with a climatological vortex that is too strong, has too low an aspect ratio, or has a westward-biased centroid usually produce fewer SSWs, whereas ones with weaker strength, high aspect ratio, and eastward-biased centroid produce more. Based on the multimodel mean, the biases of all parameters have improved across three generations of CMIPs, although persistent biases in some individual models exist across CMIPs. Intermodel relationships between the polar vortex and SST highlights the important role of the SST simulation for the stratosphere.

Brief communication: Improving ERA5-Land soil temperature in permafrost regions using an optimized multi-layer snow scheme

Abstract. We previously reported a notable warm bias in ERA5-Land soil temperature in permafrost regions that was supposedly being caused by an underestimation of snow density. In this study, we implemented and evaluated a new multi-layer snow scheme in the land surface scheme of ERA5-Land, i.e., HTESSEL, with revised snow densification parameterizations. We compared permafrost soil temperatures from the numerical experiments with observations and the original ERA5-Land with a single-layer snow scheme. The revised HTESSEL significantly improved the representation of soil temperature in permafrost regions compared to ERA5-Land. The daily warm bias in winter was reduced by about 0.6–3.0 ∘C across the 522 observing stations in high-latitude permafrost regions, and the resulting modeled near-surface permafrost extent was improved (11.0–12.9×106 km2 during 2001–2018), comparing reasonably with observed estimates for continuous and discontinuous permafrost areas. We therefore suggest that a better-resolved snow scheme with a multi-layer snow profile should be included in next-generation reanalyses as a first step towards improving the representation of permafrost.

Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale

There has been a statement that the satellite and reanalyses significantly overestimate snow depths (SDs) as compared with in-situ observations over the Tibetan Plateau (TP). The inconsistency may be partly due to representations related to different spatial resolutions. To further clarify matters of scale, this work estimates and compares the fractional snow cover (FSC) from MODIS (500 m), SDs from Sentinel-1 C-band SAR (1 km), ERA5-Land (9 km) and ERA5 (31 km), together with ground observations from GHCN-D and at 6 newly-established stations in the Namco watershed. SD and FSC from MERRA-2 (50 km) are also discussed in comparison with ERA5s. Results indicate that SD from fine resolution reanalysis has better consistency to in-situ observations over the TP. SD of ERA5-Land matches in-situ measurements better than ERA5 and MERRA-2. Overestimates of SDs in reanalyses are likely for shallow snowpack. However, underestimates are found for deep snow particularly late in the snow season. Improvements are displayed in ERA5-Land, while increased SD is attributed by altitude in fine resolution. Overall SDs of ERA5 and ERA5-Land have similar spatial distributions and annual cycle patterns over the TP, consistent with satellite-based datasets. A notable defect of ERA5s is related to delayed ablation of deep snowpack during spring and early summer. It causes cold temperature biases at surface that may impact on the land-atmosphere interaction. Snow analysis that combines the information of FSC into SD has been demonstrated in MERRA-2 data. Snow analysis has the potential to improve SDs of ERA5s over the TP.

Anomalous valley Hall effect and Nernst effect in strain engineered graphene

Anomalous valley Hall effect (AVHE), which forwards a strategy for combining valleytronics and spintronics, has recently attracted much interest. Usually, this effect is associated with the anomalous velocity acquired by the carriers due to the Berry curvature of the Bloch bands. Here we propose a new strategy to generate AVHE in a graphene-based normal/strained/normal junction, where AVHE originates from the spin-valley tunneling asymmetry for the transmission through the junction. When the system is driven by a temperature bias, an anomalous valley Nernst effect is demonstrated, in which the transverse current is completely spin- and valley-polarized simultaneously. In particular, the thermally induced longitudinal charge current can become zero with the finite transverse one, causing the ratio between them to be infinite, which is usually small for the Hall effect. It is expected that our findings could provide potential applications in valleytronics and spintronics.

Evaluation of Sea Ice Simulation of CAS-ESM 2.0 in Historical Experiment

A sea ice model is an important component of an Earth system model, which is an essential tool for the study of sea ice, including its internal processes, interactions with other components, and projected future changes. This paper evaluates a simulation of sea ice by the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM 2.0), focusing on a historical simulation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with the observations, CAS-ESM 2.0 reproduces reasonable seasonal cycle features and the climatological spatial distribution of Arctic and Antarctic sea ice, including sea ice extent (SIE), sea ice concentration, and sea ice thickness and motion. However, the SIE in CAS-ESM 2.0 is too large in winter and too low in summer in both hemispheres, indicating higher seasonal variations of the model relative to observations. Further sea ice mass budget diagnostics show that basal growth contributes most to ice increase in both hemispheres, basal melt and top melt make a comparable contribution to Arctic ice decrease, and basal melt plays a dominant role in Antarctic ice loss. This, combined with surface air temperature (SAT) and sea surface temperature (SST) biases, suggests that the excess of sea ice simulated in wintertime in both hemispheres and the lower SIE simulated in the Antarctic summer are mainly attributable to the bias in SST, whereas the lower SIE simulated in the Arctic summer is probably due to the combined effects of both the SST and SAT biases.

Accounting for Lambertian reflection in the assimilation of microwave sounding radiances over snow and sea‐ice

AbstractThis article investigates two aspects aimed at an increased use of surface‐sensitive microwave sounding radiances for numerical weather prediction (NWP), namely the assimilation of humidity‐sounding radiances from the Advanced Technology Microwave Sounder (ATMS) over snow, as well as the use of Lambertian reflection in radiative‐transfer modelling over snow and sea‐ice surfaces for temperature‐ and humidity‐sounding radiances. The Lambertian characteristics are modelled through a parameterisation of the effective zenith angle used to calculate the downwelling radiation in the radiative‐transfer model. Using Lambertian rather than specular reflection improves the forward modelling significantly for 183‐GHz humidity‐sounding channels over snow and sea‐ice in comparisons with observations, including a reduction of significant zenith‐angle‐dependent biases. For temperature‐sounding channels in the 50‐GHz band, comparisons between observations and background equivalents also suggest improvements from treating snow and sea‐ice surfaces as Lambertian or semi‐Lambertian, but the characteristics appear more variable and there are indications of other sources of error, such as biases in the skin temperature. For both spectral regions, the improvement from using nonspecular reflection is clearest for snow‐covered land surfaces. Assimilation experiments show that the addition of 183‐GHz humidity‐sounding channels from ATMS over snow‐covered land leads to a slight positive impact over higher latitudes in winter. On top of this, replacing the specular surface assumption over snow and sea‐ice with a fully Lambertian one for 183‐GHz channels on ATMS and semi‐Lambertian one for 50‐GHz channels on ATMS and the Advanced Microwave Sounding Unit–A (AMSU‐A) means that more observations pass quality control, giving a neutral to slightly positive impact at higher latitudes in winter. Combining the two changes leads to small forecast benefits that are statistically significant up to day 3 at higher latitudes.

On the Representation of Mediterranean Overflow Waters in Global Climate Models

Abstract Accurate representation of the Atlantic–Mediterranean exchange in climate models is important for a reliable simulation of the circulation in the North Atlantic Ocean. We evaluate the performance of 10 global climate models in representing Mediterranean Overflow Water (MOW) over the recent period 1986–2005 by using various performance metrics. The metrics are based on the representation of the climatological mean state and the spatiotemporal variability of temperature, salinity, and volume transports. On the basis of analyses and observations, we perform a model ranking by calculating absolute, relative, and total relative errors Ej over each performance metric and model. The majority of models simulate at least six metrics well. The equilibrium depth of the MOW, the mean Atlantic–Mediterranean exchange flow, and the dominant pattern of the MOW are represented reasonably well by most of the models. Of those models considered, MPI-ESM-MR, MPI-ESM-LR, CSIRO Mk3.6.0, and MRI-CGCM3 provide the best MOW representation (Ej = 0.14, 0.19, 0.19, and 0.25, respectively). They are thus likely to be the most suitable choices for studies of MOW-dependent processes. However, the models experience salinity, temperature, and transport biases and do not represent temporal variability accurately. The implications of our results for future model analysis of the Mediterranean Sea overflow are discussed.

On the Importance of Representing Snow Over Sea‐Ice for Simulating the Arctic Boundary Layer

AbstractCorrectly representing the snow on sea‐ice has great potential to improve cryosphere‐atmosphere coupling in forecasting and monitoring (e.g., reanalysis) applications, via improved modeling of surface temperature, albedo and emissivity. This can also enhance the all‐weather all‐surface coupled data assimilation for atmospheric satellite radiances. Using wintertime observations from two Arctic field campaigns, SHEBA and N‐ICE2015, and satellite data, we explore the merits of different approaches to represent the snow over sea‐ice in a set of 5‐day coupled forecasts. Results show that representing the snow insulation effects is essential for capturing the wintertime surface temperature variability over sea‐ice and its response to changes in the atmospheric forcing. Modeling the snow over sea‐ice improves the representation of strong cooling events, reduces surface temperature biases in clear‐sky conditions and improves the simulation of surface‐based temperature inversions. In clear‐sky conditions, when using a multi‐layer snow scheme the root‐mean‐squared error in the surface temperature is reduced by about 60% for both N‐ICE2015 and SHEBA. This study also highlights the role of compensating errors in different components of the surface energy budget in the Arctic boundary layer. During warm air intrusions, errors in the surface temperature increase when cloud phase and cloud radiative processes are misrepresented in the model, inducing large errors in the net radiative energy at the surface. This work indicates that numerical weather prediction systems can fully benefit from a better representation of snow over sea‐ice, for example, with multi‐layer snow schemes, combined with improvements to other boundary layer processes including mixed phase clouds.

COMAP Early Science. I. Overview

The CO Mapping Array Project (COMAP) aims to use line-intensity mapping of carbon monoxide (CO) to trace the distribution and global properties of galaxies over cosmic time, back to the Epoch of Reionization (EoR). To validate the technologies and techniques needed for this goal, a Pathfinder instrument has been constructed and fielded. Sensitive to CO(1–0) emission from z = 2.4–3.4 and a fainter contribution from CO(2–1) at z = 6–8, the Pathfinder is surveying 12 deg2 in a 5 yr observing campaign to detect the CO signal from z ∼ 3. Using data from the first 13 months of observing, we estimate P CO(k) = −2.7 ± 1.7 × 104 μK2 Mpc3 on scales k = 0.051 −0.62 Mpc−1, the first direct three-dimensional constraint on the clustering component of the CO(1–0) power spectrum. Based on these observations alone, we obtain a constraint on the amplitude of the clustering component (the squared mean CO line temperature bias product) of μK2, nearly an order-of-magnitude improvement on the previous best measurement. These constraints allow us to rule out two models from the literature. We forecast a detection of the power spectrum after 5 yr with signal-to-noise ratio (S/N) 9–17. Cross-correlation with an overlapping galaxy survey will yield a detection of the CO–galaxy power spectrum with S/N of 19. We are also conducting a 30 GHz survey of the Galactic plane and present a preliminary map. Looking to the future of COMAP, we examine the prospects for future phases of the experiment to detect and characterize the CO signal from the EoR.

Improving long-term temperature bias stability of an integrated optical gyroscope employing a Si3N4 resonator

The presence of polarization noise generated by the waveguide resonator limits the performance of a resonant integrated optical gyroscope (RIOG). Using silicon nitride ( Si 3 N 4 ) to fabricate a waveguide with an ultralow-aspect-ratio can result in a resonator that only supports light transmission in a single-polarization state, suppressing polarization noise. We successfully fabricated a Si 3 N 4 resonator with a bending radius of 17.5 mm, a finesse ( F ) of 150, a quality factor ( Q ) of 1.54 × 10 7 , and a propagation loss of 1.2 dB/m. The Si 3 N 4 resonator was used to construct a double closed-loop RIOG that showed long-term bias stability (3600 s) of 13.2°/h at room temperature, 14.8°/h at 40°C, 21.2°/h at 50°C, and 23.6°/h at 60°C. We believe this to be the best performance reported to date for a Si 3 N 4 resonator-based RIOG. This advancement paves the way for the wider application of RIOGs.

Evaluation of Satellite-Based Air Temperature Estimates at Eight Diverse Sites in Africa

High resolution satellite and reanalysis-based air temperature estimates have huge potential to complement the sparse networks of air temperature measurements from ground stations in Africa. The recently released Climate Hazards Center Infrared Temperature with Stations (CHIRTS-daily) dataset provides daily minimum and maximum air temperature estimates on a near-global scale from 1983 to 2016. This study assesses the performance of CHIRTS-daily in comparison with measurements from eight ground stations in diverse locations across Africa from 1983 to 2016, benchmarked against the ERA5 and ERA5-Land reanalysis to understand its potential to provide localized temperature information. Compared to ERA5 and ERA5-Land, CHIRTS-daily maximum temperature has higher correlation and lower bias of daily, annual mean maximum and annual extreme maximum temperature. It also exhibits significant trends in annual mean maximum temperature, comparable to those from the station data. CHIRTS-daily minimum temperatures generally have higher correlation, but larger bias than ERA5 and ERA5-Land. However, the results indicate that CHIRTS-daily minimum temperature biases may be largely systematic and could potentially be corrected for. Overall, CHIRTS-daily is highly promising as it outperforms ERA5 and ERA5-Land in many areas, and exhibits good results across a small, but diverse set of sites in Africa. Further studies in specific geographic areas could help support these findings.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.

Seasonal Aspects of Radiative and Advective Air Temperature Populations: A Canadian Perspective

Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts and their corresponding temperature biases and trends. The Linear Pattern Discrimination algorithm, conceptualized in a previous study, was adjusted to seasonal analysis on the equinox-to-equinox time scale. Count analysis of radiative and advective days supports the existence of two distinct thermal regimes, Spring–Summer and Fall–Winter. Further, seasonal advective counts for the majority of examined stations typically decrease in numbers. The consistently warmer winter radiative temperature extrema points to the critical role of the advective population in control of the overall temperature magnitude. Canadian northwest warming trends are found to be the highest, indicating the amplifying effect of decreasing advective counts with rapidly increasing temperatures that weaken the advective population’s moderating ability to control the magnitude of the total temperature population.

Snow Representation over Siberia in Operational Seasonal Forecasting Systems

Seasonal forecasting systems still have difficulties predicting temperature over continental regions, while their performance is better over some maritime regions. On the other hand, the land surface is a substantial source of (sub-)seasonal predictability. A crucial land surface component in focus here is the snow cover, which stores water and modulates the surface radiation balance. This paper’s goal is to attribute snow cover seasonal forecasting biases and lack of skill to either initialization or parameterization errors. For this purpose, we compare the snow representation in five seasonal forecasting systems (from DWD, ECMWF, Météo-France, CMCC, and ECCC) and their performances in predicting snow and 2-m temperature over a Siberian region against ERA5 reanalysis and station data. Although all systems use similar atmospheric and land initialization approaches and data, their snow and temperature biases differ in sign and amplitude. Too-large initial snow biases persist over the forecast period, delaying and prolonging the melting phase. The simplest snow scheme (used in DWD’s system) shows too-early and fast melting in spring. However, systems including multi-layer snow schemes (Météo-France and CMCC) do not necessarily perform better. Both initialization and parameterization are causes of snow biases, but, depending on the system, one can be more dominant.

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

Abstract. Remotely sensed Earth observations have many missing values. The abundance and often complex patterns of these missing values can be a barrier for combining different observational datasets and may cause biased estimates of derived statistics. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatial or temporal interpolation or by upscaling approaches in which complete donor variables are used to infer missing values. However, these approaches typically do not account for information that may be present in other observed variables that also have missing values. Here we propose CLIMFILL (CLIMate data gap-FILL), a multivariate gap-filling procedure that combines kriging interpolation with a statistical gap-filling method designed to account for the dependence across multiple gappy variables. In a first stage, an initial gap fill is constructed for each variable separately using state-of-the-art spatial interpolation. Subsequently, the initial gap fill for each variable is updated to recover the dependence across variables using an iterative procedure. Estimates for missing values are thus informed by knowledge of neighbouring observations, temporal processes, and dependent observations of other relevant variables. CLIMFILL is tested using gap-free ERA-5 reanalysis data of ground temperature, surface-layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These variables were matched with corresponding remote sensing observations and masked where the observations have missing values. In this “perfect dataset approach” CLIMFILL can be evaluated against the original, usually not observed part of the data. We show that CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations in the gap-filled dataset. Correlation between original ERA-5 data and gap-filled ERA-5 data is high in many regions, although it shows artefacts of the interpolation procedure in large gaps in high-latitude regions during winter. Bias and noise in gappy satellite-observable data is reduced in most regions. A case study of the European 2003 heatwave shows how CLIMFILL reduces biases in ground temperature and surface-layer soil moisture induced by the missing values. Furthermore, in idealized experiments we see the impact of fraction of missing values and the complexity of missing value patterns to the performance of CLIMFILL, showing that CLIMFILL for most variables operates at the upper limit of what is possible given the high fraction of missing values and the complexity of missingness patterns. Thus, the framework can be a tool for gap filling a large range of remote sensing observations commonly used in climate and environmental research.

Will Increasing Climate Model Resolution Be Beneficial for ENSO Simulation?

AbstractIncreasing climate model resolution offers multifaceted benefits, such as improving modeled tropical cyclones. However, the extent to which it benefits El Niño‐Southern Oscillation (ENSO) simulation remains unknown. Here, comprehensive information on the sensitivity of ENSO performance to various resolutions is provided, based on a multi‐model and multi‐resolution ensemble of global coupled models. Overall, the reduced model biases of the equatorial sea surface temperature (SST) and precipitation mean state in higher‐resolution models may be attributed to increased oceanic resolution, and thus, better resolved eddy‐driven heat transport. ENSO spatial patterns were reproduced clearer in the eddy‐present models, likely due to the improved mean state and associated surface thermodynamic feedback. However, increasing atmospheric resolution alone deteriorates ENSO asymmetry, which may be due to the degradation of nonlinear atmospheric feedback. It remains challenging to alleviate the SST−shortwave‐flux feedback bias, which is a major source of too‐weak net heat flux feedback, irrespective of model resolution.

Climate projections over the Great Lakes Region: using two-way coupling of a regional climate model with a 3-D lake model

Abstract. Warming trends in the Laurentian Great Lakes and surrounding areas have been observed in recent decades, and concerns continue to rise about the pace and pattern of future climate change over the world's largest freshwater system. To date, most regional climate models used for Great Lakes projections either neglected the lake-atmosphere interactions or are only coupled with a 1-D column lake model to represent the lake hydrodynamics. This study presents a Great Lakes climate change projection that has employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes. Using the three carefully selected Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs), we show that the GLARM ensemble average substantially reduces surface air temperature and precipitation biases of the driving GCM ensemble average in present-day climate simulations. The improvements are not only displayed from an atmospheric perspective but are also evident in the accurate simulations of lake temperature and ice coverage. We further present the GLARM projected climate change for the mid-21st century (2030–2049) and the late 21st century (2080–2099) in the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios. Under RCP 8.5, the Great Lakes basin is projected to warm by 1.3–2.1 ∘C by the mid-21st century and 4.1–5.0 ∘C by the end of the century relative to the early century (2000–2019). Moderate mitigation (RCP 4.5) reduces the mid-century warming to 0.8–1.8 ∘C and late-century warming to 1.8–2.7 ∘C. Annual precipitation in GLARM is projected to increase for the entire basin, varying from 0 % to 13 % during the mid-century and from 9 % to 32 % during the late century in different scenarios and simulations. The most significant increases are projected in spring and fall when current precipitation is highest and a minimal increase in winter when it is lowest. Lake surface temperatures (LSTs) are also projected to increase across the five lakes in all of the simulations, but with strong seasonal and spatial variability. The most significant LST increases occur in Lakes Superior and Ontario. The strongest warming is projected in spring that persists into the summer, resulting from earlier and more intense stratification in the future. In addition, diminishing winter stratification in the future suggests the transition from dimictic lakes to monomictic lakes by the end of the century. In contrast, a relatively smaller increase in LSTs during fall and winter is projected with heat transfer to the deep water due to the strong mixing and energy required for ice melting. Correspondingly, the highest monthly mean ice cover is projected to reduce to 3 %–15 % and 10 %–40 % across the lakes by the end of the century in RCP 8.5 and RCP 4.5, respectively. In the coastal regions, ice duration is projected to decrease by up to 60 d.

An Intercomparison of Antarctic NWP during the Austral Summer Special Observing Period for the Year of Polar Prediction

Abstract The special observing periods (SOPs) of the Year of Polar Prediction present an opportunity to assess the skill of numerical weather prediction (NWP) models operating over the Antarctic, many of which assimilated additional observations during an SOP to produce some of the most observationally informed model output to date for the Antarctic region and permitting closer examination of model performance under various configurations and parameterizations. This intercomparison evaluates six NWP models spanning global and limited domains, coupled and uncoupled, operating in the Antarctic during the austral summer SOP between 16 November 2018 and 15 February 2019. Model performance varies regionally between each model and parameter; however, the majority of models were found to be warm biased over the continent with respect to ERA5 at analysis, some with biases growing to 3.5 K over land after 48 h. Temperature biases over sea ice were found to be strongly correlated between analysis and 48 h in uncoupled models, but that this correlation can be reduced through coupling to a sea ice model. Surface pressure and 500-hPa geopotential height forecasts and biases were found to be strongly correlated over open ocean in all models, and wind speed forecasts were found to be generally more skillful at higher resolutions with the exception of fast modeled winds over sloping terrain in PolarWRF. Surface sensible and latent heat flux forecasts and biases produced diverse correlations, varying by model, parameter, and gridcell classification. Of the models evaluated, those which couple atmosphere, sea ice, and ocean typically exhibited stronger skill. Significance Statement We evaluated the performance of six numerical weather prediction models operating over the Antarctic during the Year of Polar Prediction austral summer special observing period (16 November 2018–15 February 2019). Our analysis found that several models were as much as 3.5 K warmer than the reference analysis (ERA5) at 48 h over land and were strongly correlated over sea ice in uncoupled models; however, this correlation is reduced through coupling to a sea ice model. Surface pressure biases are communicated to the midtroposphere over the ocean at larger spatial scales, while higher resolution showed an increase in positive wind biases at longer forecasts. Surface turbulent heat fluxes produced complex correlations with other forecast parameters, which should be quantified in future studies. Coupled models that included an ocean/sea ice component typically performed better; providing evidence that the inclusion of such components leads to improved model performance, even at short time scales such as these.