Air Temperature Bias Research Articles

Large-Scale Surface Air Temperature Bias in Summer over the CONUS and its Relationship to Tropical Central Pacific Convection in the UFS Prototype 8

Large-Scale Surface Air Temperature Bias in Summer over the CONUS and its Relationship to Tropical Central Pacific Convection in the UFS Prototype 8

Assessing the Tropospheric Temperature and Humidity Simulations in CMIP3/5/6 Models Using the AIRS Obs4MIPs V2.1 Data

AbstractIn this study, the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.1 tropospheric air temperature, specific humidity, and relative humidity data are utilized to evaluate the global tropospheric temperature and humidity simulations in the fully coupled global climate models from the Coupled Model Intercomparison Project phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6), and possible simulation improvement in CMIP6 models in comparison to CMIP3 and CMIP5 models. Our analyses indicate that all three phases of CMIP models share similar tropospheric air temperature, specific humidity, and relative humidity biases in their multi‐model ensemble means relative to AIRS. Cold biases up to 4 K and positive relative humidity biases up to 20% are found in the free troposphere almost globally with maxima over the mid‐latitude storm tracks. Warm biases up to 2 K are seen over the Southern Ocean in the lower troposphere. Positive specific and relative humidity biases exist over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere, which are related to the double‐intertropical convergence zone bias in the models. Both the air temperature and specific humidity biases are important to the relative humidity biases except in the tropical free troposphere where the specific humidity biases dominate. The tropospheric air temperature, specific humidity, and relative humidity biases are reduced from CMIP3 to CMIP5 and to CMIP6 at almost all pressure levels except at 300 hPa for specific humidity and in the boundary layer for relative humidity.

Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps

Abstract. We examine the impact of horizontal resolution and model time step on the climate of the OpenIFS version 43r3 atmospheric general circulation model. A series of simulations for the period 1979–2019 are conducted with various horizontal resolutions (i.e. ∼100, ∼50, and ∼25 km) while maintaining the same time step (i.e. 15 min) and using different time steps (i.e. 60, 30, and 15 min) at 100 km horizontal resolution. We find that the surface zonal wind bias is significantly reduced over certain regions such as the Southern Ocean and the Northern Hemisphere mid-latitudes and in tropical and subtropical regions at a high horizontal resolution (i.e. ∼25 km). Similar improvement is evident too when using a coarse-resolution model (∼100 km) with a smaller time step (i.e. 30 and 15 min). We also find improvements in Rossby wave amplitude and phase speed, as well as in weather regime patterns, when a smaller time step or higher horizontal resolution is used. The improvement in the wind bias when using the shorter time step is mostly due to an increase in shallow and mid-level convection that enhances vertical mixing in the lower troposphere. The enhanced mixing allows frictional effects to influence a deeper layer and reduces wind and wind speed throughout the troposphere. However, precipitation biases generally increase with higher horizontal resolutions or smaller time steps, whereas the surface air temperature bias exhibits a small improvement over North America and the eastern Eurasian continent. We argue that the bias improvement in the highest-horizontal-resolution (i.e. ∼25 km) configuration benefits from a combination of both the enhanced horizontal resolution and the shorter time step. In summary, we demonstrate that, by reducing the time step in the coarse-resolution (∼100 km) OpenIFS model, one can alleviate some climate biases at a lower cost than by increasing the horizontal resolution.

On the importance of the humidity flux for the surface mass balance in the accumulation zone of the Greenland Ice Sheet

Abstract. It is highly uncertain how the humidity flux between the snow surface and the atmosphere contributes to the surface mass balance (SMB) of the interior Greenland Ice Sheet (GrIS). Due to sparse observations, evaluations of the simulated humidity flux are limited. Model-based estimates of the humidity flux contribution to the SMB are, therefore, unconstrained and even disagree in magnitude and sign. In this study, we evaluate the regional climate model MAR at the EGRIP (East Greenland Ice-Core Project) site in the accumulation zone of the GrIS. We use a combined dataset of continuous one-level bulk estimates of the humidity flux covering the period of May 2016–August 2019 and eddy-resolving eddy-covariance humidity flux measurements from all four summer seasons. In summer, we document a bias of too little sublimation (−1.3 W m−2, −1.65 mm w.e.) caused by a cold bias in both air and surface temperature, leading to a reduced humidity gradient. In winter, MAR overestimates vapor deposition by about 1 order of magnitude. This is a consequence of an overestimated temperature gradient in too stable atmospheric conditions compared to observations. Both systematic errors cause a large discrepancy in the annual net humidity flux between the model and observations of −9 mm w.e. yr−1. Remarkably, the simulated net annual humidity flux contributes positively to the SMB, contrary to observations documenting a net sublimation flux. We correct the systematic errors by applying a simple but effective correction function to the simulated latent heat flux. Using this correction, we find that 5.1 % of the annual mass gain at the EGRIP site sublimates again, and 4.3 % of the total mass gain is deposited vapor from the near-surface air. The estimated net humidity flux contribution to the annual SMB is about −1 % (net sublimation) compared to +5.6 % for the uncorrected simulation. In summer, the corrected MAR simulation shows that vapor deposition accounts for 9.6 % of the total mass gain and that 31 % of the total mass gain at the EGRIP site sublimates again. The net fluxes contribute to −32 % of the summer SMB. These results demonstrate that the humidity flux is a major driver of the summer SMB in the accumulation zone of the GrIS and highlight that even small changes could increase its importance for the annual SMB in a warming climate.

From Public Forecasts to Occupational Heat Safety Management: Evaluating the Effectiveness of the National Weather Service's Operational Heat Risk Forecasts.

Heat poses a major environmental risk to occupational safety, necessitating timely insights into associated risks to safeguard workers. In June 2022, the National Weather Service (NWS) initiated operational wet bulb globe temperature (WBGT) forecasts, offering valuable information for heat risk management. This study evaluates the effectiveness of NWS WBGT forecasts, aiming to identify potential areas of caution and improvements for their application for occupational safety management. To this end, the study examines 1.3 million hourly historical NWS WBGT forecast data, comparing it with observed data from 252 weather stations across the US during the summer of 2023. The results offer key insights, revealing that: (1) the accuracy of NWS WBGT forecasts is influenced more by the times of interest than by the forecast horizons; (2) NWS WBGT forecast accuracy varies across different climates in the US, with air temperature bias being the most influential factor in this inaccuracy; and (3) while NWS WBGT forecasts accurately identify the lowest heat risks (i.e. no heat risk), their performance decreases at higher risk levels, emphasizing the importance of careful interpretation in safety management. These insights offer guidance for more cautious interpretations of NWS WBGT forecasts and lay the foundation for future studies on leveraging operational weather forecasting services in effective heat mitigation strategies.

Difference in boreal winter predictability between two dynamical cores of Community Atmosphere Model 5

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FVSE) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001/2002–2017/2018, are validated using Japanese 55 year reanalysis data. It turns out that the SE (−4.27 °C) has a smaller cold bias in boreal-winter surface air temperature (SAT) than the FV (−5.17 °C) and FVSE (−5.29 °C), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter SAT, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FVSE), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FVSE and FV. Conversely, in East Asia, FV and FVSE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.

Regional and Teleconnected Impacts of Solar Radiation‐Topography Interaction Over the Tibetan Plateau

AbstractSolar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

To aspirate or not to aspirate – Impact of active versus passive ventilation on urban heat (island) indicators

The present paper reports on the impact of ventilation mode – active versus passive – of instrument screens for air temperature measurements on canopy urban heat (island) indicators. This is done using air temperature data gathered in the Antwerp (Belgium) area in July 2013, in an experimental set-up composed of an urban and a nearby rural climate station, each equipped with both actively and passively ventilated temperature sensors. The resulting data shows an air temperature bias between the passively and actively ventilated measurements of up to approximately 2 °C, the highest values occurring under conditions of a high solar radiation load and low wind speed. Yet, the canopy urban heat island (UHI) increment, i.e., the urban-rural air temperature difference, is hardly affected by the ventilation method, which is ascribed to the fact that high UHI increments occur mainly at night when, in absence of solar radiation, active ventilation has a lesser influence on measured temperature. Conversely, urban heat indicators involving daytime air temperature, such as heatwave degree days (HWDD) or Steadman's apparent temperature, tend to produce overly high values when use is made of passively ventilated temperature measurements.

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

AbstractRadiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.

Improving snow albedo parameterization scheme based on remote sensing data

Snow albedo is an important parameter for determining the energy budget in high-latitude regions in winter, but it is often overestimated in the model and causes a cold bias of air temperature. To address this issue, we propose a new approach to improve the Canadian Land Surface Scheme (CLASS) snow albedo parameterization in Noah-MP by updating the initial value of vegetation snow albedo based on remote sensing data. The modified CLASS scheme resulted in a 0.04 decrease in root mean squared error (RMSE) of the albedo, and the mean bias was reduced by 0.07, representing relative decreases of 37.3% and 89.1%, respectively. In addition, the simulation errors for the upward radiation and sensible heat flux were significantly reduced. Furthermore, the bias and RMSE of air temperature were reduced by 12.9% and 7.9%, respectively, compared to those in the original CLASS scheme. These qualitative and quantitative analyses demonstrated that our modified CLASS snow albedo parameterization scheme has a robust and positive impact on the simulation of snow cover areas. Our approach provides a preliminary investigation for improving snow albedo estimation, and we plan to conduct a widespread simulation and introduce new vegetation data into the model to improve the estimation of albedo in future research.

Performance analysis of the urban climate model MUKLIMO_3 for three extreme heatwave events in Bern

Extreme heatwaves represent a health hazard that is expected to increase in the future, and which particularly affects urban populations worldwide due to intensification by urban heat islands. To analyze the impact of such extreme heatwaves on urban areas, urban climate models are a valuable tool. This study examines the performance of the urban climate model MUKLIMO_3 in modelling spatial air temperature patterns in the greater urban area of Bern, Switzerland, a city in complex topography, during three distinct extreme heatwaves in 2018 and 2019 over a total of 23 days. The model is validated using low-cost air temperature data from 79 (2018) and 84 (2019) measurement sites. The intercomparison of the three extreme heatwaves shows that during the first extreme heatwave 2019 at lower elevation regions in the outskirts of the city, modelled air temperature was higher than observation, which was likely due to pronounced mesoscale cold air advection. During calm and dry days, the air temperature distribution was modelled realistically over all three extreme heatwaves investigated. During daytime, modelled air temperatures were lower across all evaluation sites and all extreme heatwaves when compared to the measured values, with highest median air temperature differences of −3.7 K to −4.8 K found in the late afternoon. At night, MUKLIMO_3 generally shows a slowed cooling, so that higher air temperatures were modelled when compared to measured values, with median air temperature biases of +1.5 K to +2.8 K at midnight. By sunrise, the model biases continuously decreased, so that the lowest air temperatures at 7 a.m. were modelled with a bias of +0.2 K to +0.7 K. Peak biases exceed 7 K both during day and night. In sum, our results show that MUKLIMO_3 allows to realistically model the urban air temperature distributions during the peaks of the heatwaves investigated with the highest day and night air temperatures, which may assist in the development of heat mitigation measures to reduce the impacts of heat extremes and improve public health in cities with complex topography.

Assessing the impact of measurement errors in the calculation of CWSI for characterizing the water status of several crop species

AbstractCanopy temperature is generally accepted as an indirect but rapid, accurate, and large-scale indicator of crop water status and is, therefore, proposed to monitor irrigation needs. Crop Water Stress Index (CWSI) is the most widely used among the existing thermal-based indicators, and its links with water stress have been demonstrated. When calculating CWSI using the empirical approach, the differential between canopy and air temperature is normalized by two thresholds, also known as baselines. The Non-water stress baseline (NWSB) in the empirical approach is calculated as the relationship between Tc–Ta (°C) and the vapor pressured deficit (VPD, kPa) for well-irrigated crops. The baselines display different slopes depending on the species, which have a significant impact on the computed CWSI. This study analyzed the resulting errors on CWSI due to the measurement errors of critical inputs needed for its calculation. Six crop species were selected according to their NWSB with slopes that range from − 0.5 to − 3 °C·kPa−1 and used for this analysis, assuming measurement errors ranging 0.2–1 °C for Ta, 0.25–2 °C for Tc, and 5–10% for relative humidity (RH). It was concluded that the effects observed on CWSI are heavily dependent on the slope of the NWSB and therefore vary across species. The calculation was very sensitive to the bias in air and canopy temperature. These errors were maximal as the slope of the NWSB was less steep. When the VPD ranged from 2 to 6.6 kPa, an error of 1 °C in measuring the air temperature affected CWSI between 28 and 83% in orange, which is the species displaying the minimum slope (− 0.5 °C kPa−1). On the contrary, crops with steeper baseline slopes such as squash (− 3 °C kPa−1) showed errors ranging between 2 and 8% for the same VPD interval. This differences among the different crops species considered in this study may be related to the contrasting coupling of the species to the atmosphere, that determines the influence of vapor pressure on the transpiration rate. This study highlights the importance of reliable climatic data and the need for accurate calibrated thermal sensors to calculate CWSI accurately.

Evaluation of Albedo Schemes in WRF Coupled with Noah-MP on the Parlung No. 4 Glacier

Meteorological variables (e.g., air temperature (T2), radiation flux, and precipitation) determine the evolution of glacier mass and characteristics. Observations of these variables are not available with adequate spatial coverage and spatiotemporal resolution over the Tibetan Plateau. Albedo is the key factor of net radiation and is determined by the land cover and snow-related variables. This study focuses on evaluating the performance of the albedo parameterization scheme in WRF coupled with Noah-MP in terms of glacio-meteorological variables, by conducting experiments applying the standard surface albedo scheme with the default vegetation and corrected to ice cover and the modified glacial albedo scheme to the Parlung No. 4 Glacier in the 2016 ablation season. In situ glacio-meteorological element observations and MODIS-retrieved albedo are selected to assess the performance of the model. The key results are as follows. First, compared to the air temperature bias of 1.56 °C in WRF applying the standard surface albedo scheme and the default vegetation cover, realistic land-use categories considerably reduce the model warm bias on the glacier. The model using realistic land-use categories yields similar T2 diurnal patterns to the observations, with a mean bias of only −0.5 °C, no matter which glacial albedo scheme is implemented. Second, the default glacial albedo scheme gives a rather high albedo value of 0.68, causing an apparent underestimation of the net shortwave radiation and net radiation; the modified glacial albedo scheme gives a mean albedo value of 0.35, close to the in situ observations, helping to relieve underestimations of net shortwave radiation and net radiation. Compared with the MODIS albedo of the glacier, WRF applying the default glacial albedo scheme apparently overestimates the albedo with a mean error of 0.18, while WRF applying the modified glacial albedo scheme slightly underestimates the albedo with a mean error of only −0.08. Third, the mean net radiation flux (142 W m−2) and high ground heat flux (182 W m−2) values that were estimated by WRF applying the corrected land cover and the modified glacial albedo scheme result in the heating of the glacier surface and subsurface, causing ice melt and the liquid water content to increase more quickly and preferentially, equating to an estimated ice thickness decrease of 1 m by mid-June in the ablation region. Our study confirms the ability of the WRF model to reproduce glacio-meteorological variables as long as a reasonable glacial albedo scheme and the corrected land cover is applied and provides a theoretical reference for researchers that are committed to further improvement of the glacial albedo scheme.

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.

Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

It is important to quantify changes in the local meteorological observational environment (MOE) around weather stations if we are to obtain accurate assessments of the regional warming of the surface air temperature (SAT) in relation to urbanization bias. Current studies often use two-dimensional parameters (e.g., the land surface temperature, land use/land cover and the normalized difference vegetation index) to characterize the local MOE. Most of the existing models of the relationship between urbanization bias in SAT series and MOE parameters are linear regression models, which ignore the non-linear driving effect of MOE changes on SAT series. By contrast, there is a lack of three-dimensional parameters in the characterization of the morphological features of the MOE. Changes in the MOE related to urbanization lead to uncertainties in the contribution of SAT series on different scales and we need to introduce vertical structure indexes to enrich the three-dimensional spatial morphology of MOE parameters. The non-linear response of urbanization bias in SAT series to three-dimensional changes in the MOE and its scale dependence should be explored by coupling computational fluid dynamics model simulations with machine learning.

The Interaction between the Nocturnal Amazonian Low-Level Jet and Convection in CESM

AbstractA 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, and 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–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 h 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–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.

The impact of data assimilation into the meteorological WRF model on birch pollen modelling

We analyse the impact of ground-based data assimilation to the Weather Research and Forecasting (WRF) meteorological model on parameters relevant for birch pollen emission calculations. Then, we use two different emission databases (BASE – no data assimilation, OBSNUD – data assimilation for the meteorological model) in the chemical transport model and evaluate birch pollen concentrations. Finally, we apply a scaling factor for the emissions (BASE and OBSNUD), based on the ratio between simulated and observed seasonal pollen integral (SPIn) to analyse its impact on birch concentrations over Central Europe. Assimilation of observational data significantly reduces model overestimation of air temperature, which is the main parameter responsible for the start of pollen emission and amount of released pollen. The results also show that a relatively small bias in air temperature from the model can lead to significant differences in heating degree days (HDD) value. This may cause the HDD threshold to be attained several days earlier/later than indicated from observational data which has further impact on the start of pollen emission. Even though the bias for air temperature was reduced for OBSNUD, the model indicates a start for the birch pollen season that is too early compared to observations. The start date of the season was improved at two of the 11 stations in Poland. Data assimilation does not have a significant impact on the season's end or SPIn value. The application of the SPIn factor for the emissions results in a much closer birch pollen concentration level to observations even though the factor does not improve the start or end of the pollen season. The post-processing of modelled meteorological fields, such as the application of bias correction, can be considered as a way to further improve the pollen emission modelling.

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

AbstractSurface air temperatures affect a diverse set of physical and biological systems in many parts of the world. For regional‐scale studies, gridded surface air temperature data sets are frequently used as input variables. Here we evaluate 10 commonly used gridded air temperature products with spatial resolutions ranging from 0.1° × 0.1° to 5.0° × 5.0°, relative to observations from in situ weather stations on the Tibetan Plateau. Gridded temperatures are consistently lower, with mean annual air temperature biases ranging from −4.68 °C to −1.72 °C, and root mean square error (RMSE) from 3.24 °C to 6.11 °C. The mean biases of mean seasonal air temperatures for spring, summer, autumn, and winter are −3.21 °C, −2.98 °C, −2.89 °C, and −2.98 °C, respectively, and RMSEs of 4.81 °C, 4.39 °C, 4.3 °C, and 4.7 °C. Of all 10 products, the China meteorological forcing data set compares best with observations. Annual and season temperatures all increase significantly during 1980–2018 on the Tibetan Plateau, with winter dominating the warming rate.

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ~0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

Clouds and Radiation Processes in Regional Climate Models Evaluated Using Observations Over the Ice‐free Arctic Ocean

AbstractThe presence of clouds in the Arctic regulates the surface energy budget (SEB) over the sea‐ice surface and the ice‐free ocean. Following several previous field campaigns, the cloud‐radiation relationship, including cloud vertical structure and phase, has been elucidated; however, modeling of this relationship has matured slowly. In recognition of the recent decline in the Arctic sea‐ice extent, representation of the cloud system in numerical models should consider the effects of areas covered by sea ice and ice‐free areas. Using an in situ stationary meteorological observation data set obtained over the ice‐free Arctic Ocean by the Japanese Research Vessel Mirai (September 2014), coordinated evaluation of six regional climate models (RCMs) with nine model runs was performed by focusing on clouds and the SEB. The most remarkable findings were as follows: (1) reduced occurrence of unstable stratification with low‐level cloud water in all models in comparison to the observations, (2) significant differences in cloud water representations between single‐ and double‐moment cloud schemes, (3) extensive differences in partitioning of hydrometeors including solid/liquid precipitation, and (4) pronounced lower‐tropospheric air temperature biases. These issues are considered as the main sources of SEB uncertainty over ice‐free areas of the Arctic Ocean. The results from a coupled RCM imply that the SEB is constrained by both the atmosphere and the ocean (and sea ice) with considerable feedback. Coordinated improvement of both stand‐alone atmospheric and coupled RCMs would promote a more comprehensive and improved understanding of the Arctic air‐ice‐sea coupled system.