Variability Of Near-surface Temperature Research Articles

Seasonal amplification of subweekly temperature variability over extratropical Southern Hemisphere land masses

Temperature variability has substantial socioeconomic impacts through its association with the frequency and severity of heat extremes. Under anthropogenic influence, climate models project seasonally-dependent amplifications of near-surface temperature variability over some sectors of the Southern Hemisphere, and robust positive trends have already been observed in recent decades. Here we show that the amplification of subweekly temperature variability simulated by the multi-model ensemble mean of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) over South Africa, Australia, and South America is often substantially smaller than in reanalyses in recent decades, reaching a similar amplification only at the end of the 21st century due to a weaker amplification of subweekly variance generation efficiency. Analysis of a large model ensemble indicates that this discrepancy may be due to internal climatic variability suggesting that the recent rapid amplification seen in reanalyses may slow down or even temporarily reverse in the near future.

Performance evaluation and ranking of CMIP6 global climate models over Vietnam

Abstract This study comprehensively assesses the performance of 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and their ensemble mean (ENS_MEAN) over Vietnam. The spatiotemporal variability of near-surface temperature and precipitation is thoroughly evaluated for the 30-year historical period of 1985–2014. Results show that the models can reasonably reproduce the observational annual cycles and spatial distribution of temperature and precipitation, though their performances vary across the seven climatic sub-regions of Vietnam. Due to their coarse resolutions, the models produce warm biases over certain highland and mountainous areas, and many of them cannot reproduce the rainy season shift from summer in most sub-regions toward the end of the year in Central Vietnam. Finally, these results are summarized, and a performance score is assigned to each model, inferring a ranking. The top three models are EC-Earth3-Veg, EC-Earth3, and HadGEM3-GC31-MM. Although created by simply averaging all the models, ENS_MEAN can compete with the best GCMs and ranks 4th overall. In the same model family, the higher-resolution experiment exhibits a higher ranking than the lower-resolution one. The results of this study can provide a reference for the choice of appropriate CMIP6 GCMs in the upcoming downscaling activities in Vietnam.

Characteristics of atmospheric boundary layer structure and its influencing factors under different sea and land positions in Europe

This study identifies quantitatively the dominant contributions of meteorological factors on the development of the boundary layer heights (BLH) in the European region, based on 32 years (1990−2021) of radiosonde observations. The spatial variability of the BLH is further discussed by location, by classifying recording stations as inland, coastal, or bay. We find that the BLH in Europe varies considerably from day to night and with the seasons. Nighttime BLH is higher in winter and lower in summer, with the highest BLH recorded at coastal stations. Daytime BLH at coastal stations shows a bimodal structure with peaks in spring and autumn; at inland and bay stations, daytime BLH is lower in winter and higher in summer. The daily amplitudes of BLH at the inland and bay stations are stronger than those at coastal stations. Based on our multiple linear regression analysis and our decoupling analysis of temperature and specific humidity, we report that the development of the nighttime BLH at all types of stations is strongly dominated by the variations of surface wind speed (and, at coastal stations, wind directions). The main contributors to daytime BLH are the near-surface temperature variability at most coastal and inland stations, and, at most bay stations, the variation of the near-surface specific humidity.

Effect of land–atmosphere process parameterizations on the PM simulation of a river valley city with complex topography

Realistic representation of land–atmosphere processes (LAPs) plays a significant role in meteorology and consequent air quality simulations. In this study, 15 series setups, different combinations of five planetary boundary layers (PBL) with compatible surface layer (SL) schemes, and three land surface models (LSMs) were designed in the WRF-Chem model to perform a sensitivity study assessing the influence of LAPs on simulations. Sensitivity tests were conducted over the river valley city at 1 km resolution under synoptically quiescent conditions on December 1–6, 2019. The results were evaluated via comparison with five meteorological and 29 air quality stations. Statistical indicators were used to understand the physical mechanisms of various LAPs which impact air pollution diffusion and to identify the preferred configuration. Our studies revealed that, under synoptically quiescent conditions over complex topography, adequate representations of physical processes occurred in lower atmosphere have critical implications on numerical simulation of regional meteorology and air quality. The simulations seemed more sensitive to the LSMs than to PBL schemes. The NoahMP land surface model exhibited better performance than the RUC and SLAB models, likely because it effectively captured the daily variations in near-surface temperature and wind direction. LAPs had a significant effect on the air quality simulations. The differences in correlation coefficients and daily particulate matter (PM) concentrations between the model simulations in response to the LAP configurations reached 86%–94% and 42%–48% respectively. The MYN_N configuration, namely the NoahMP land surface model coupled with the MYNN PBL scheme, improved the accuracy of the modeled PM concentration and constituted a reference for air quality prediction over complex topography under calm synoptic conditions.

Diurnal and Seasonal Variability of Near-Surface Temperature and Humidity in the Maritime Continent

Abstract This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature TWB when compared with the temperature and dewpoint temperature TD. The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure. Significance Statement This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.

Evaluation of Wind and Solar Insolation Influence on Ocean Near-Surface Temperature from In Situ Observations and the Geostationary Himawari-8 Satellite

The skin sea surface temperature (SST) observed by the geostationary Himawari-8 satellite and bulk SST, including four in situ observations from ships, drifters, Argo, and buoys constitute more than 90,000 SST pairs used to analyze near-surface temperature variations. From July 2015 to May 2022, an average SST bias of 0.10 °C and root mean square error of 0.99 °C were observed in the waters adjacent to Taiwan. This study effectively observed that the skin effect generated by ocean wind and solar shortwave radiation caused the occurrence of a cool skin layer and diurnal warm layer (DWL), and 90% of the SST bias was in a range of −1.55~1.71 °C. In the daytime, the skin layer received solar shortwave radiation, thus increasing temperature and causing a DWL. With the increase in insolation, the SST bias in the DWL became more obvious. During winter, strong wind, or low shortwave radiation, the DWL may disappear and turn into a cool skin layer. At night, the near-surface SST was dominated by the cool skin effect, but the DWL generated in the daytime would remain if the wind speed was weak. However, the different hydrological characteristics of the observation position and its distance from the coast could affect the results of the skin effect. Whether there is a rapid change in ocean stratification in a spatial grid of nearly four square kilometers needs to be explored in the future.

Computation of vertical fluid mobility of CO_{2}, methane, hydrogen and hydrocarbons through sandstones and carbonates

Over the last decade, there has been an irreversible shift from hydrocarbon exploration towards carbon storage, low-carbon energy generation and hydrogen exploration. Whilst basin modelling techniques may be used to predict the migration of hydrocarbons through sedimentary basins on geological timescales, there remains little understanding of how fluids behave at the basin scale on present-day timescales. We apply the Darcy flow equation to present an algorithm to determine the basin-scale mobilities and maximum vertical velocity, v_{max}, of CO_{2}, methane, hydrogen and hydrocarbons with depth for sandstone and carbonate. v_{max} for CO_{2} and methane are on scales of m/year, whilst values for hydrocarbon fluids are an order of magnitude smaller than for other fluids. Our results indicate that the fluid mobility of subsurface CO_{2} may be sensitive to surface and near-surface temperature variations. v_{max} for hydrogen is approximately 2–10 times greater than hydrocarbon fluids, yielding important consequences for the future use of basin modelling software for determining hydrogen migration for exploration and storage.

Estimation of the terms acting on local 1 h surface temperature variations in Paris region: the specific contribution of clouds

Abstract. Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

Rapid Arctic warming and its link to the waviness and strength of the westerly jet stream over West Asia

Rapid sea-ice loss and warming of the Arctic within the past few decades have contributed to changes in the strength and location of midlatitude jet streams. Using ERA-Interim data for the period 1979–2018, warming of the Arctic compared to midlatitudes and its impact on the waviness and strength of the westerly jet stream over West Asia are investigated. Pre- and post-Arctic amplification periods are defined as 1979–1993 and 2004–2018, respectively. Near-surface temperature variability is decreased in the Arctic in the post-Arctic amplification period when a larger area of open ocean has been exposed to the atmosphere. In both pre- and post-Arctic amplification periods, near-surface temperature variabilities in spring and summer were less than half as much of its variability in winter. During the period 1979–2018, both the Arctic and West Asia have warmed, with much higher warming of the Arctic in autumn and winter. Indeed, in contrast to the continental cooling pattern observed in parts of North and Northeast Asia, West Asia has warmed in recent decades. Following a decrease in the meridional temperature gradient, the upper-tropospheric jet stream over West Asia has been reduced in the post-Arctic amplification period in all seasons except autumn. In the post-Arctic amplification period, the zonal wind shear has been also reduced over West Asia in all seasons, while waviness of the Z500 isopleth has been increased in winter. A higher meridional mid-tropospheric waviness is found over the study region (20–75°E) in summer compared to winter because in summer the western part of this region is influenced by a strong subtropical high pressure, while its eastern part is influenced by the Indian monsoon and associated trough of low pressure. • Near-surface temperature variability is decreased in the Arctic in the post-Arctic amplification period • In contrast to cooling of some midlatitude regions, West Asia has warmed in recent decades • Upper-tropospheric jet stream over West Asia has been reduced in the post-Arctic amplification period • Zonal wind shear has been reduced over West Asia in all seasons, but waviness of Z500 isopleth has been increased in winter

Modeling of land-use and land-cover change impact on summertime near-surface temperature variability over the Delhi–Mumbai Industrial Corridor

This study explores the impact of land-use and land-cover (LULC) changes due to different urbanization scenarios in the Delhi–Mumbai Industrial Corridor (DMIC) region on surface air temperature by conducting sensitivity studies with the Weather Research and Forecasting (WRF) model. First, a sensitivity analysis conducted using the planetary boundary layer (PBL) and surface layer (SL) physical parameterizations available in WRF. Results show that the model performs better when a combination of the Mellor–Yamada–Janjic PBL and ETA SL schemes used. Then, numerical experiments performed using WRF with the above configuration and three different LULC scenarios to assess the possible impact of urbanization on near-surface temperature during May. A total of ten simulations were conducted for the May month of 2005–2014 to estimate the statistical and physical robustness of results. Each of these simulations considered as ensemble member while computing average values. The study shows that there is a significant effect of LULC change mainly on the minimum temperature (Tmin) with an increase of 2.9–3.7℃ over the DMIC region. The impact is less on maximum temperature, with a minor rise in 0.1–0.3℃. The probable reason for the increase in minimum temperature is likely due to a decrease in latent heat flux and an increase in sensible heat flux. The impact on air temperature was found to be prominent over existing urban clusters than the surrounding built environment; also, this effect is additive and more pronounced with the extreme LULC change scenario.

Understanding the linkage between soil moisture variability and temperature extremes over the Indian region

Soil moisture (SM) and near-surface temperature variations are known to be linked through land–atmosphere interactions. While previous investigators have examined the association between temperature extremes and large-scale atmospheric circulation variability, the role of land–atmosphere coupling on temperature extremes over the monsoon-dominated region of India is not well understood. This study presents an analysis of hydro-meteorological datasets for the 67-year period (1948–2014) to assess the impact of long-term soil moisture changes on temperature extremes over the Indian region. Firstly, our findings show that the hot-spot of land–atmosphere coupling located across north-central India (NCI) is a region where SM variations can significantly influence temperature extremes. We further note that the NCI region experienced a significant declining trend in SM (about 1.1 mm/decade) during 1948–2014, in association with the decreasing trend of monsoon precipitation. Our findings suggest that the long-term decrease of SM over the NCI has favored increased incidence of temperature extremes through strengthening (weakening) of sensible (latent) heat fluxes; while the loss of soil moisture memory has additionally promoted increased variability of temperature extremes. The frequency, duration and variability of extreme temperatures are found to increase significantly by 1–2 occurrences, 5–6 days and 43%, respectively, in association with a decrease of 10 mm SM over NCI. The Generalized Extreme Value (GEV) distributions are fitted to extreme temperature duration (ExTD) using SM as a covariate to quantify the role of SM on temperature extremes over NCI. GEV analysis reveals that drier SM conditions (10th percentile) lead to an increase in the 67-year return value of ExTD by 9–10 days, relative to wet SM conditions (90th percentile) over NCI. Furthermore, it is interesting to note that the rise in temperature extremes over NCI in the recent three decades has been more prominent during the monsoon and post-monsoon seasons as compared to the pre-monsoon months.

Spatial variability of near-surface temperature over the coastal mountains in southern Chile (38\xb0S)

The spatial distribution of the near-surface air temperature over a coastal mountain range in southern Chile [Nahuelbuta Mountains (NM), 38°S, maximum height 1300-m ASL] is investigated using in situ measurements, satellite-derived land-surface temperature, and simulations during the austral winter of 2011. Based on a few selected but representative cases, we found that under rainy conditions—either at day or night—temperature decreases with height close to the moist adiabatic lapse rate (~6.5 °C/km). Likewise, the temperature tends to follow the dry adiabat (~9.8 °C/km) during daytime under dry- and clear-skies conditions. During clear-skies nights, the temperature also decreases with height over the southeastern side of NM, but it often increases (at about 8 °C/km) over the northwestern side of the mountains. This temperature inversion extends up to about 700-m ASL leading to an average temperature contrast of about 7 °C between the northwestern and southeastern sides of Nahuelbuta by the end of dry nights. These dawns also feature substantial temperature differences (>10 °C) among closely located stations at a same altitude. High-resolution numerical simulations suggest that upstream blocking of the prevailing SE flow, hydrostatic mountain waves, and strong downslope winds is responsible for such distinctive nocturnal temperature distribution.

Sensitivity of Attribution of Anthropogenic Near-Surface Warming to Observational Uncertainty

The impact of including comprehensive estimates of observational uncertainties on a detection and attribution analysis of twentieth-century near-surface temperature variations is investigated. The error model of HadCRUT4, a dataset of land near-surface air temperatures and sea surface temperatures, provides estimates of measurement, sampling, and bias adjustment uncertainties. These uncertainties are incorporated into an optimal detection analysis that regresses simulated large-scale temporal and spatial variations in near-surface temperatures, driven by well-mixed greenhouse gas variations and other anthropogenic and natural factors, against observed changes. The inclusion of bias adjustment uncertainties increases the variance of the regression scaling factors and the range of attributed warming from well-mixed greenhouse gases by less than 20%. Including estimates of measurement and sampling errors has a much smaller impact on the results. The range of attributable greenhouse gas warming is larger across analyses exploring dataset structural uncertainty. The impact of observational uncertainties on the detection analysis is found to be small compared to other sources of uncertainty, such as model variability and methodological choices, but it cannot be ruled out that on different spatial and temporal scales this source of uncertainty may be more important. The results support previous conclusions that there is a dominant anthropogenic greenhouse gas influence on twentieth-century near-surface temperature increases.

Robust Future Changes in Temperature Variability under Greenhouse Gas Forcing and the Relationship with Thermal Advection

Abstract Recent temperature extremes have highlighted the importance of assessing projected changes in the variability of temperature as well as the mean. A large fraction of present-day temperature variance is associated with thermal advection, as anomalous winds blow across the land–sea temperature contrast, for instance. Models project robust heterogeneity in the twenty-first-century warming pattern under greenhouse gas forcing, resulting in land–sea temperature contrasts increasing in summer and decreasing in winter and the pole-to-equator temperature gradient weakening in winter. In this study, future changes in monthly variability of near-surface temperature in the 17-member ensemble ESSENCE (Ensemble Simulations of Extreme Weather Events under Nonlinear Climate Change) are assessed. In winter, variability in midlatitudes decreases whereas in very high latitudes and the tropics it increases. In summer, variability increases over most land areas and in the tropics, with decreasing variability in high latitude oceans. Multiple regression analysis is used to determine the contributions to variability changes from changing temperature gradients and circulation patterns. Thermal advection is found to be of particular importance in the Northern Hemisphere winter midlatitudes, where the change in mean state temperature gradients alone could account for over half the projected changes. Changes in thermal advection are also found to be important in summer in Europe and coastal areas, although less so than in winter. Comparison with CMIP5 data shows that the midlatitude changes in variability are robust across large regions, particularly high northern latitudes in winter and middle northern latitudes in summer.

Randomised multichannel singular spectrum analysis of the 20th century climate data

In this article, we introduce a new algorithm called randomised multichannel singular spectrum analysis (RMSSA), which is a generalisation of the traditional multichannel singular spectrum analysis (MSSA) into problems of arbitrarily large dimension. RMSSA consists of (1) a dimension reduction of the original data via random projections, (2) the standard MSSA step and (3) a recovery of the MSSA eigenmodes from the reduced space back to the original space. The RMSSA algorithm is presented in detail and additionally we show how to integrate it with a significance test based on a red noise null-hypothesis by Monte-Carlo simulation. Finally, RMSSA is applied to decompose the 20th century global monthly mean near-surface temperature variability into its low-frequency components. The decomposition of a reanalysis data set and two climate model simulations reveals, for instance, that the 2–6 yr variability centred in the Pacific Ocean is captured by all the data sets with some differences in statistical significance and spatial patterns.

Physics of Changes in Synoptic Midlatitude Temperature Variability

AbstractThis paper examines the physical processes controlling how synoptic midlatitude temperature variability near the surface changes with climate. Because synoptic temperature variability is primarily generated by advection, it can be related to mean potential temperature gradients and mixing lengths near the surface. Scaling arguments show that the reduction of meridional potential temperature gradients that accompanies polar amplification of global warming leads to a reduction of the synoptic temperature variance near the surface. This is confirmed in simulations of a wide range of climates with an idealized GCM. In comprehensive climate simulations (CMIP5), Arctic amplification of global warming similarly entails a large-scale reduction of the near-surface temperature variance in Northern Hemisphere midlatitudes, especially in winter. The probability density functions of synoptic near-surface temperature variations in midlatitudes are statistically indistinguishable from Gaussian, both in reanalysis data and in a range of climates simulated with idealized and comprehensive GCMs. This indicates that changes in mean values and variances suffice to account for changes even in extreme synoptic temperature variations. Taken together, the results indicate that Arctic amplification of global warming leads to even less frequent cold outbreaks in Northern Hemisphere winter than a shift toward a warmer mean climate implies by itself.

Attribution of observed historical near‒surface temperature variations to anthropogenic and natural causes using CMIP5 simulations

We have carried out an investigation into the causes of changes in near‒surface temperatures from 1860 to 2010. We analyze the HadCRUT4 observational data set which has the most comprehensive set of adjustments available to date for systematic biases in sea surface temperatures and the CMIP5 ensemble of coupled models which represents the most sophisticated multi‒model climate modeling exercise yet carried out. Simulations that incorporate both anthropogenic and natural factors span changes in observed temperatures between 1860 and 2010, while simulations of natural factors do not warm as much as observed. As a result of sampling a much wider range of structural modeling uncertainty, we find a wider spread of historic temperature changes in CMIP5 than was simulated by the previous multi‒model ensemble, CMIP3. However, calculations of attributable temperature trends based on optimal detection support previous conclusions that human‒induced greenhouse gases dominate observed global warming since the mid‒20th century. With a much wider exploration of model uncertainty than previously carried out, we find that individually the models give a wide range of possible counteracting cooling from the direct and indirect effects of aerosols and other non‒greenhouse gas anthropogenic forcings. Analyzing the multi‒model mean over 1951–2010 (focusing on the most robust result), we estimate a range of possible contributions to the observed warming of approximately 0.6 K from greenhouse gases of between 0.6 and 1.2 K, balanced by a counteracting cooling from other anthropogenic forcings of between 0 and −0.5 K.

Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods

This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales.

Modelling atmospheric structure, cloud and their response to CCN in the central Arctic: ASCOS case studies

Abstract. Observations made during late summer in the central Arctic Ocean, as part of the Arctic Summer Cloud Ocean Study (ASCOS), are used to evaluate cloud and vertical temperature structure in the Met Office Unified Model (MetUM). The observation period can be split into 5 regimes; the first two regimes had a large number of frontal systems, which were associated with deep cloud. During the remainder of the campaign a layer of low-level cloud occurred, typical of central Arctic summer conditions, along with two periods of greatly reduced cloud cover. The short-range operational NWP forecasts could not accurately reproduce the observed variations in near-surface temperature. A major source of this error was found to be the temperature-dependant surface albedo parameterisation scheme. The model reproduced the low-level cloud layer, though it was too thin, too shallow, and in a boundary-layer that was too frequently well-mixed. The model was also unable to reproduce the observed periods of reduced cloud cover, which were associated with very low cloud condensation nuclei (CCN) concentrations (<1 cm−3). As with most global NWP models, the MetUM does not have a prognostic aerosol/cloud scheme but uses a constant CCN concentration of 100 cm−3 over all marine environments. It is therefore unable to represent the low CCN number concentrations and the rapid variations in concentration frequently observed in the central Arctic during late summer. Experiments with a single-column model configuration of the MetUM show that reducing model CCN number concentrations to observed values reduces the amount of cloud, increases the near-surface stability, and improves the representation of both the surface radiation fluxes and the surface temperature. The model is shown to be sensitive to CCN only when number concentrations are less than 10–20 cm−3.

Observing Local-Scale Variability of Near-Surface Temperature and Humidity Using a Wireless Sensor Network

AbstractIn this paper the influence of surface type, wind speed, and other environmental conditions on near-surface air temperature, specific humidity, and surface temperature is studied. A wireless sensor network consisting of 13 low-cost meteorological stations was set up as a 2.3-km-long double transect in western Germany during the Fluxes and Patterns in the Soil–Vegetation–Atmosphere Scheme (FLUXPAT2009) campaign. This deployment covered various surface types, including a small river. It was found that the air temperature was mainly influenced by the distance to the river and that its variability is controlled by the wind speed. During the night, a pool of cold air formed in the valley close to the water. The specific humidity is also governed by proximity to the river, especially during the night and for low wind speeds. In contrast, the differences in surface temperature were caused by different land cover. These results can be confirmed by a cluster analysis. Setting up 13 stations in a relatively small area is not always feasible. In this study, an estimation of the error that is made by considering the effect of a reduced number of stations is given. Use of only a single station results in an error of 0.86 K in air temperature, 0.67 g kg−1 in specific humidity, and 1.4 K in surface temperature.