Near-surface Temperature Trends Research Articles

Ionospheric electric potential as an alternative indicator of solar effect on the lower atmosphere

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

Электрический потенциал ионосферы — альтернативный индикатор солнечного воздействия на нижнюю атмосферу

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

Effects of Local Vegetation and Regional Controls in Near-Surface Air Temperature for Southeastern Brazil

The spatial range of near-surface air temperature average and trends for Southeast Brazil in recent decades motivated us to investigate the causality of local vegetation and other geophysical controls at the regional scale to explain the spatial variability of the average maximum and minimum temperature (Tmax and Tmin). We used measurements from 52 weather stations between 1985 and 2010. Using linear regression, NDVI and cloud cover were significant to explain spatial variability of Tmax and Tmin. With the Generalized Additive Model (GAM), we improved temperature-dependent relationships with regional geophysical controls, and local scale NDVI. The modeling of Tmax and Tmin showed non-linear and combined relationships with geographical position (lat,lon) jointly expressing the effects of zonality and continentality, and NDVI at distances of 300 m and 3000 m. For Tmin, geographical position and altitude responded with an amplitude of ≃5 °C each, and NDVI with ≃3 °C. Similarly, the geographical position and altitude were significant for Tmax, with an amplitude of ≃5 °C each, and cloud cover with ≃3.5 °C. Our findings help to clarify the local scale controls of near-surface air temperature and stress the need to increase resilience against adversities of global climate change and increasing urbanization, by providing metrics to predict the effects of nature-based solutions within the urban space.

Modelling the historic and projected near-surface temperature trends across Canada

ABSTRACT This article explores the impact that Shared Socioeconomic Pathways (SSPs) 1–2.6 and 5–8.5 will have on near-surface temperature across Canada. This is achieved through the use of model ensembles, made up of CMIP5 data, to establish both the historic record of near-surface temperature across Canada, as well as how this is predicted to change in the future given the two SSP emission scenarios. The conclusions drawn from this modelling and analysis is valuable in the context of sustainable development and climate mitigation and adaptation discussions. Specifically, an understanding of the spatial extent of warming trends across Canada will allow for a more specific and tailored approach to be taken towards adaptation and ensure appropriate measures are taken in good time.

Atmospheric trends over the Arctic Ocean in simulations from the Coordinated Regional Downscaling Experiment (CORDEX) and their driving GCMs

The Arctic Coordinated Regional Downscaling Experiment (Arctic-CORDEX) uses regional climate models (RCMs) to downscale selected Fifth Coupled Model Intercomparison Project simulations, allowing trend validation and projection on subregional scales. For 1986–2015, the CORDEX seasonal-average near-surface temperature (tas), wind speed (sfcWind), precipitation (pr) and snowfall (prsn) trends are generally consistent with analyses/observations for the Arctic Ocean regions considered. The projected Representative Concentration Pathway 8.5 (RCP8.5) 2016–2100 subregional annual tas trends range from 0.03 to 0.18 K/year. Projected annual pr and prsn trends have a large inter-model spread centered around approximately 5.0 × 10–8 mm/s/year and −5.0 × 10–8 mm/s/year, respectively, while projected sfcWind summer and winter trends range between 0.0 and 0.4 m/s/year. For all variables except prsn, and sometimes total precipitation, the driving general circulation model (GCM) dominates the trends, however there is a tendency for the GCMs to underestimate the sfcWind trends compared to the RCMs. Subtracting the Arctic-Ocean mean from subregional trends reveals a consistent, qualitative anomaly pattern in several variables and seasons characterized by greater-than or average trends in the central and Siberian Arctic Ocean and lesser or average trends in the Atlantic Sector and the Bering Sea, related to summer sea-ice trends. In particular, a strong proportional relationship exists between the summer sea-ice concentration and fall tas and sfcWind trend anomalies. The RCP4.5 annual, multi-model mean trends are 35–55% of the corresponding RCP8.5 trends for most variables and subregions.

Impacts of Arctic Sea Ice on Cold Season Atmospheric Variability and Trends Estimated from Observations and a Multi-model Large Ensemble

AbstractTo examine the atmospheric responses to Arctic sea-ice variability in the Northern Hemisphere cold season (October to following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily-varying sea-ice, sea-surface temperature, and radiative forcings prescribed during the 1979-2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multi-model ensemble mean (MMEM) shows decreasing sea-level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea-ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drives a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual co-variability between sea-ice extent in the Barents-Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the co-variability in MMEMs. The interannual sea-ice decline followed by a negative North Atlantic Oscillation-like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea-ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

Recent Near-surface Temperature Trends in the Antarctic Peninsula from Observed, Reanalysis and Regional Climate Model Data

This study investigates the recent near-surface temperature trends over the Antarctic Peninsula. We make use of available surface observations, ECMWF’s ERA5 and its predecessor ERA-Interim, as well as numerical simulations, allowing us to contrast different data sources. We use hindcast simulations performed with Polar-WRF over the Antarctic Peninsula on a nested domain configuration at 45 km (PWRF-45) and 15 km (PWRF-15) spatial resolutions for the period 1991-2015. In addition, we include hindcast simulations of KNMI-RACMO21P obtained from the CORDEX-Antarctica domain (~50 km) for further comparisons. Results show that there is a marked windward warming trend except during summer. This windward warming trend is particularly notable in the autumn season and likely to be associated with the recent deepening of the Amundsen/Bellingshausen Sea low and warm advection towards the Antarctic Peninsula. On the other hand, an overall summer cooling is characterized by the strengthening of the Weddell Sea low as well as an anticyclonic trend over the Amundsen Sea accompanied by northward winds. The persistent cooling trend observed at the Larsen Ice Shelf station is not captured by ERA-Interim, whereas hindcast simulations indicate that there is a clear pattern of windward warming and leeward cooling. Furthermore, larger temporal correlations and lower differences exhibited by PWRF-15 illustrate the existence of the added value in the higher spatial resolution simulation.

Temperature monitoring in mountain regions using reanalyses: lessons from the Alps

The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20–60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.

Global Contributions of Incoming Radiation and Land Surface Conditions to Maximum Near-Surface Air Temperature Variability and Trend.

The evolution of near‐surface air temperature is influenced by various dynamical, radiative, and surface‐atmosphere exchange processes whose contributions are still not completely quantified. Applying stepwise multiple linear regression to Coupled Model Intercomparison Project phase 5 (CMIP5) model simulations and focusing on radiation (diagnosed by incoming shortwave and incoming longwave radiation) and land surface conditions (diagnosed by soil moisture and albedo) about 79% of the interannual variability and 99% of the multidecadal trend of monthly mean daily maximum temperature over land can be explained. The linear model captures well the temperature variability in middle‐to‐high latitudes and in regions close to the equator, whereas its explanatory potential is limited in deserts. While radiation is an essential explanatory variable over almost all of the analyzed domain, land surface conditions show a pronounced relation to temperature in some confined regions. These findings highlight that considering local‐to‐regional processes is crucial for correctly assessing interannual temperature variability and future temperature trends.

Impact of evolving greenhouse gas forcing on the warming signal in regional climate model experiments

Variations in the atmospheric concentrations of greenhouse gases (GHG) may not be included as external forcing when running regional climate models (RCMs); at least, this is a non-regulated, non-documented practice. Here we investigate the so far unexplored impact of considering the rising evolution of the CO2, CH4, and N2O atmospheric concentrations on near-surface air temperature (TAS) trends, for both the recent past and the near future, as simulated by a state-of-the-art RCM over Europe. The results show that the TAS trends are significantly affected by 1–2 K century−1, which under 1.5 °C global warming translates into a non-negligible impact of up to 1 K in the regional projections of TAS, similarly affecting projections for maximum and minimum temperatures. In some cases, these differences involve a doubling signal, laying further claim to careful reconsideration of the RCM setups with regard to the inclusion of GHG concentrations as an evolving external forcing which, for the sake of research reproducibility and reliability, should be clearly documented in the literature.

Observed warming over northern South America has an anthropogenic origin

We investigate whether the recently observed trends in daily maximum and minimum near-surface air temperature (Tmax and Tmin, respectively) over South America (SA) are consistent with the simulated response of Tmin and Tmax to anthropogenic forcing. Results indicate that the recently observed warming in the dry seasons is well beyond the range of natural (internal) variability. In the wet season the natural modes of variability explain a substantial portion of Tmin and Tmax variability. We demonstrate that the large-scale component of greenhouse gas (GHG) forcing is detectable in dry-seasonal warming. However, none of the global and regional climate change projections reproduce the observed warming of up to 0.6 K/Decade in Tmax in 1983–2012 over northern SA during the austral spring (SON). Thus, besides the global manifestation of GHG forcing, other external drivers have an imprint. Using aerosols-only forcing simulations, our results provide evidence that anthropogenic aerosols also have a detectable influence in SON and that the indirect effect of aerosols on cloud’s lifetime is more compatible with the observed record. In addition, there is an increasing trend in the observed incoming solar radiation over northern SA in SON, which is larger than expected from natural (internal) variability alone. We further show that in the dry seasons the spread of projected trends based on the RCP4.5 scenario derived from 30 CMIP5 models encompasses the observed area-averaged trends in Tmin and Tmax. This may imply that the observed excessive warming in the dry seasons serve as an illustration of plausible future expected change in the region.

An “Observational Large Ensemble” to Compare Observed and Modeled Temperature Trend Uncertainty due to Internal Variability

Abstract Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

Assessing atmospheric temperature data sets for climate studies

Observed near-surface temperature trends during the period 1979–2014 show large differences between land and ocean, with positive values over land (0.25–0.27 °C/decade) that are significantly larger than over the ocean (0.06–0.12 °C/decade). Temperature trends in the mid-troposphere of 0.08-0.11 °C/decade, on the other hand, are similar for both land and ocean and agree closely with the ocean surface temperature trend. The lapse rate is consequently systematically larger over land than over the ocean and also shows a positive trend in most land areas. This is puzzling as a response to external warming, such as from increasing greenhouse gases, is broadly the same throughout the troposphere. The reduced tropospheric warming trend over land suggests a weaker vertical temperature coupling indicating that some of the processes in the planetary boundary layer such as inversions have a limited influence on the temperature of the free atmosphere. Alternatively, the temperature of the free atmosphere is influenced by advection of colder tropospheric air from the oceans. It is therefore suggested to use either the more robust tropospheric temperature or ocean surface temperature in studies of climate sensitivity. We also conclude that the European Centre for Medium-Range Weather Forecasts Reanalysis Interim can be used to obtain consistent temperature trends through the depth of the atmosphere, as they are consistent both with near-surface temperature trends and atmospheric temperature trends obtained from microwave sounding sensors.

Large-Scale Atmospheric Warming in Winter and the Arctic Sea Ice Retreat

Abstract The ongoing shrinkage of the Arctic sea ice cover is likely linked to the global temperature rise, the pronounced warming in the Arctic, and possibly weather anomalies in the midlatitudes. By evaluating independent components of global atmospheric energy anomalies in winters from 1980 to 2015, the study finds the link between the sea ice melting in the Arctic and the combination of only three well-known atmospheric oscillation patterns approximating observed spatial variations of near-surface temperature trends in winter. The three patterns are the North Atlantic Oscillation (NAO), Scandinavian blocking (SB), and El Niño–Southern Oscillation (ENSO). The first two are directly related to the ongoing sea ice cover shrinkage in the Barents Sea and the hemispheric increase of near-surface temperature. By independent dynamical processes they connect the sea ice melting and related atmospheric perturbations in the Arctic either with the negative phase of the NAO or the negative trend of atmospheric temperatures over the tropical Pacific. The study further shows that the ongoing sea ice melting may often imply the formation of large-scale circulation patterns bringing the recent trend of colder winters in densely populated areas like Europe and North America.

Observational evidence of temperature trends at two levels in the surface layer

Abstract. Long-term surface air temperatures at 1.5 m screen level over land are used in calculating a global average surface temperature trend. This global trend is used by the IPCC and others to monitor, assess, and describe global warming or warming hiatus. Current knowledge of near-surface temperature trends with respect to height, however, is limited and inadequately understood because surface temperature observations at different heights in the surface layer of the world are rare especially from a high-quality and long-term climate monitoring network. Here we use high-quality two-height Oklahoma Mesonet observations, synchronized in time, fixed in height, and situated in relatively flat terrain, to assess temperature trends and differentiating temperature trends with respect to heights (i.e., near-surface lapse rate trend) over the period 1997 to 2013. We show that the near-surface lapse rate has significantly decreased with a trend of −0.18 ± 0.03 °C (10 m)−1 per decade indicating that the 9 m height temperatures increased faster than temperatures at the 1.5 m screen level and/or conditions at the 1.5 m height cooled faster than at the 9 m height. However, neither of the two individual height temperature trends by themselves were statistically significant. The magnitude of lapse rate trend is greatest under lighter winds at night. Nighttime lapse rate trends were significantly more negative than daytime lapse rate trends and the average lapse rate trend was three times more negative under calm conditions than under windy conditions. Our results provide the first observational evidence of near-surface temperature changes with respect to height that could enhance the assessment of climate model predictions.

An Evaluation of Surface Atmospheric Changes over the Arctic Ocean for 2000–09 Using Recent Reanalyses

Abstract The authors examine five recent reanalysis products [NCEP Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Japanese 25-year Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Arctic System Reanalysis (ASR)] for 1) trends in near-surface radiation fluxes, air temperature, and humidity, which are important indicators of changes within the Arctic Ocean and also influence sea ice and ocean conditions, and 2) fidelity of these atmospheric fields and effects for an extreme event: namely, the 2007 ice retreat. An analysis of trends over the Arctic for the past decade (2000–09) shows that reanalysis solutions have large spreads, particularly for downwelling shortwave radiation. In many cases, the differences in significant trends between the five reanalysis products are comparable to the estimated trend within a particular product. These discrepancies make it difficult to establish a consensus on likely changes occurring in the Arctic solely based on results from reanalyses fields. Regarding the 2007 ice retreat event, comparisons with remotely sensed estimates of downwelling radiation observations against these reanalysis products present an ambiguity. Remotely sensed observations from a study cited herewith suggest a large increase in downwelling summertime shortwave radiation and decrease in downwelling summertime longwave radiation from 2006 and 2007. On the contrary, the reanalysis products show only small gains in summertime shortwave radiation, if any; however, all the products show increases in downwelling longwave radiation. Thus, agreement within reanalysis fields needs to be further checked against observations to assess possible biases common to all products.

The Role of the Mean State of Arctic Sea Ice on Near-Surface Temperature Trends

Abstract Century-scale global near-surface temperature trends in response to rising greenhouse gas concentrations in climate models vary by almost a factor of 2, with greatest intermodel spread in the Arctic region where sea ice is a key climate component. Three factors contribute to the intermodel spread: 1) model formulation, 2) control climate state, and 3) internal climate variability. This study focuses on the influence of Arctic sea ice in the control climate on the intermodel spread in warming, using idealized 1% yr−1 CO2 increase simulations of 33 state-of-the-art global climate models, and combining sea ice–temperature relations on local to large spatial scales. On the Arctic mean scale, the spread in temperature trends is only weakly related to ice volume or area in the control climate, and is probably not dominated by internal variability. This suggests that other processes, such as ocean heat transport and meteorological conditions, play a more important role in the spread of long-term Arctic warming than control sea ice conditions. However, on a local scale, sea ice–warming relations show that in regions with more sea ice, models generally simulate more warming in winter and less warming in summer. The local winter warming is clearly related to control sea ice and universal among models, whereas summer sea ice–warming relations are more diverse, and are probably dominated by differences in model formulation. To obtain a more realistic representation of Arctic warming, it is recommended to simulate control sea ice conditions in climate models so that the spatial pattern is correct.

Temperature Trends in the NARCCAP Regional Climate Models

The skill of six regional climate models (RCMs) in reproducing short-term (24-yr), observed, near-surface temperature trends when driven by reanalysis is examined. The RCMs are part of the North American Regional Climate Change Assessment Program (NARCCAP). If RCMs can reproduce observed temperature trends, then they are, in a way, demonstrating their ability to capture a type of climate change, which may be relevant to their ability to credibly simulate anthropogenic climate changes under future emission scenarios. This study finds that the NARCCAP RCMs can simulate some resolved-scale temperature trends, especially those seen recently in spring and, by and large, in winter. However, results in other seasons suggest that RCM performance in this measure may be dependent on the type and strength of the forcing behind the observed trends.

Vertical structure of recent arctic warming from observed data and reanalysis products

Spatiotemporal patterns of recent (1979–2008) air temperature trends are evaluated using three reanalysis datasets and radiosonde data. Our analysis demonstrates large discrepancies between the reanalysis datasets, possibly due to differences in the data assimilation procedures as well as sparseness and inhomogeneity of high-latitude observations. We test the robustness of arctic tropospheric warming based on the ERA-40 dataset. ERA-40 Arctic atmosphere temperatures tend to be closer to the observed ones in terms of root mean square error compared to other reanalysis products used in the article. However, changes in the ERA-40 data assimilation procedure produce unphysical jumps in atmospheric temperatures, which may be the likely reason for the elevated tropospheric warming trend in 1979–2002. NCEP/NCAR Reanalysis data show that the near-surface upward temperature trend over the same period is greater than the tropospheric trend, which is consistent with direct radiosonde observations and inconsistent with ERA-40 results. A change of sign in the winter temperature trend from negative to positive in the late 1980s is documented in the upper troposphere/lower stratosphere with a maximum over the Canadian Arctic, based on radiosonde data. This change from cooling to warming tendency is associated with weakening of the stratospheric polar vortex and shift of its center toward the Siberian coast and possibly can be explained by the changes in the dynamics of the Arctic Oscillation. This temporal pattern is consistent with multi-decadal variations of key arctic climate parameters like, for example, surface air temperature and oceanic freshwater content. Elucidating the mechanisms behind these changes will be critical to understanding the complex nature of high-latitude variability and its impact on global climate change.

Air Temperature Variability in Illinois Based on Weather Station Records and the North American Regional Reanalysis from 1979 to 2006

Spatial and temporal near-surface air temperature variabilities and trends were analyzed for 30 locations in Illinois based on annual data derived from station records and the North American Regional Reanalysis (NARR) dataset from 1979 to 2006. A high correlation was found between the two datasets for interannual variability at most locations. Temperatures were generally higher at urban stations than non-urban stations, while non-urban NARR data points showed higher temperatures than urban data points. Differences in medians were not statistically significant in either dataset. Significant positive temperature trends were found in the majority of the weather stations and in all NARR data points, with generally stronger trends in the NARR data. Observed trends from the station records were generally stronger in metropolitan areas and weaker for non-urban areas, but the reanalysis data did not show a remarkable difference between urban and non-urban trends.