Observed Surface Air Temperature Research Articles

Likely Ranges of Climate Change in Bolivia

AbstractBolivia is facing numerous climate-related threats, ranging from water scarcity due to rapidly retreating glaciers in the Andes to a partial loss of the Amazon forest in the lowlands. To assess what changes in climate may be expected in the future, 35 global circulation models (GCMs) from the third and fifth phases of the Coupled Model Intercomparison Project (CMIP3/5) were analyzed for the Bolivian case. GCMs were validated against observed surface air temperature, precipitation, and incoming shortwave (SW) radiation for the period 1961–90. Weighted ensembles were developed, and climate change projections for five emission scenarios were assessed for 2070–99. GCMs revealed an overall cold, wet, and positive-SW-radiation bias and showed no substantial improvement from the CMIP3 to the CMIP5 ensemble for the Bolivian case. Models projected an increase in temperature (2.5°–5.9°C) and SW radiation (1%–5%), with seasonal and regional differences. In the lowlands, changes in annual rainfall remained uncertain for CMIP3 whereas CMIP5 GCMs were more inclined to project decreases (−9%). This pattern also applied to most of the Amazon basin, suggesting a higher risk of partial biomass loss for the CMIP5 ensemble. Both ensembles agreed on less rainfall (−19%) during drier months (June–August and September–November), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (January–March). In the Andes, CMIP3 GCMs tended toward less rainfall (−9%) whereas CMIP5 tended toward more (+20%) rainfall during parts of the wet season. The findings presented here may provide inputs for studies of climate change impact that assess how resilient human and natural systems are under different climate change scenarios.

Modeling and understanding persistence of climate variability

In this study, two parsimonious statistical representations of climate variability on interannual to multidecadal timescales are compared: the short‐memory first order autoregressive representation (AR1) and the long‐memory “power law” representation. Parameters for each statistical representation are fitted to observed surface air temperature at each spatial point. The parameter estimates from observations are found in general to be captured credibly in the Coupled Model Intercomparison Project 3 (CMIP3) simulations. The power law representation provides an upper bound and the AR1 representation provides a lower bound on persistence as measured by the lag‐one autocorrelation. Both representations fit the data equally well according to goodness‐of‐fit‐tests. Comparing simulations with and without external radiative forcings shows that anthropogenic forcing has little effect on the measures of persistence considered (for detrended data). Given that local interannual to multi decadal climate variability appears to be more persistent than an AR1 process and less persistent than a power law process, it is concluded that both representations are potentially useful for statistical applications. It is also concluded that current climate simulations can well represent interannual to multidecadal internal climate persistence in the absence of natural and anthropogenic radiative forcing, at least to within observational uncertainty.

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next- generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/ 1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R 2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R 2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R 2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

A correspondence between the model ensemble simulations and observations of temperature changes on the territory of Russia

The results are presented of the statistical analysis of correspondence between the model simulations and observations of temperature changes on the territory of Russia. Three model ensembles are considered, differing in the level of taking account of the impact of external forcings on the climate system of the Earth. For each of them, the statistical correspondence is estimated between the observed surface air temperature variations in the second half of the 20th century and at the beginning of the 21st century and model simulations taking account of the natural variability typical of the climate system. The analysis demonstrated that, in spite of the uncertainties associated with the differences in the representation of anthropogenic and natural external forcings on the climate in model simulations as well as with the imperfection of climate models and with internal variability of the climate system, the model experiments enable to obtain the relevant information both on the temporal evolution of temperature changes on the territory of Russia and on their spatial peculiarities.

Improved constraints on 21st‐century warming derived using 160 years of temperature observations

Projections of 21st century warming may be derived by using regression‐based methods to scale a model's projected warming up or down according to whether it under‐ or over‐predicts the response to anthropogenic forcings over the historical period. Here we apply such a method using near surface air temperature observations over the 1851–2010 period, historical simulations of the response to changing greenhouse gases, aerosols and natural forcings, and simulations of future climate change under the Representative Concentration Pathways from the second generation Canadian Earth System Model (CanESM2). Consistent with previous studies, we detect the influence of greenhouse gases, aerosols and natural forcings in the observed temperature record. Our estimate of greenhouse‐gas‐attributable warming is lower than that derived using only 1900–1999 observations. Our analysis also leads to a relatively low and tightly‐constrained estimate of Transient Climate Response of 1.3–1.8°C, and relatively low projections of 21st‐century warming under the Representative Concentration Pathways. Repeating our attribution analysis with a second model (CNRM‐CM5) gives consistent results, albeit with somewhat larger uncertainties.

Comparison of ERA‐40, ERA‐Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland

AbstractSurface air temperatures modelled by ERA‐40, ERA‐Interim and (NCEP)/(NCAR) reanalysis (NNRP‐1) have been compared with observations at 11 synoptic stations in Ireland over the period 1989–2001. The three reanalysis datasets show good agreement with the observed data and with each other. Slopes of the least‐squares line to scatter plots of reanalysis data versus observational data show small differences between the three reanalyses, with ERA‐40, ERA‐Interim and NNRP‐1 slopes ranging between (0.79–1.06) ± 0.01, (0.83–1.01) ± 0.01 and (0.76–0.98) ± 0.01, respectively. Summary statistics and the monthly mean temperatures over the 1989–2001 period showed that the reanalyses were significantly warmer in winter than the observations, which resulted in best fit lines with slopes consistently less than unity. ERA‐Interim was slightly better than both ERA‐40 and NNRP‐1 at modelling winter temperatures and it had higher correlation coefficients with the observations. All three reanalyses use different grid sizes and types. Subsequent regridding of the ERA‐Interim and NNRP‐1 data to the ERA‐40 grid showed that the grid difference had no significant influence on the results. Comparison of ERA‐Interim and NNRP‐1 data with the air temperatures at four marine buoys around the Irish coast for the period 2001–2005 showed that the reanalyses modelled colder winter temperatures than the observations; resulting in best fit lines with slopes consistently greater than unity. The slopes for NNRP‐1 and ERA‐Interim at the marine buoys, respectively, averaged 1.09 ± 0.04 and 1.10 ± 0.05 while the slopes at the four land stations over the same period averaged 0.87 ± 0.02 and 0.89 ± 0.02, respectively. We believe that this pattern results from the difference in the treatment of land and sea surfaces in the reanalysis datasets. Copyright © 2010 Royal Meteorological Society

The WAMME regional model intercomparison study

Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.

Sensitivity of surface air temperature change to land use/cover types in China

Using CRU high resolution grid observational temperature and ERA40 reanalysis surface air temperature data during 1960–1999, we investigated the sensitivity of surface air temperature change to land use/cover types in China by subtracting the reanalysis from the observed surface air temperature (observation minus reanalysis, OMR). The results show that there is a stable and systemic impact of land use/cover types on surface air temperature. The surface warming of each land use/cover type reacted differently to global warming. The OMR trends of unused land (⩾0.17 °C/decade), mainly comprised by sandy land, Gobi and bare rock gravel land, are obviously larger than those of the other land use/cover types. The OMR over grassland, farmland and construction land shows a moderate decadal warmingabout 0.12°C/decade, 0.10°C/decade, 0.12°C/decade, respectively. Woodland areas do not show a significant warming trend (0.06°C/decade). The overall assessment indicates that the surface warming is larger for areas that are barren and anthropogenically developed. The better the vegetation cover, the smaller the OMR warming trend. Responses of surface air temperature to land use/cover types with similar physical and chemical properties and biological processes have no significant difference. The surface air temperature would not react significantly until the intensity of land cover changes reach a certain degree. Within the same land use/cover type, areas in eastern China with intensive human activities exhibit larger warming trend. The results provide observational evidence for modeling research on the impact of land use/cover change on regional climate. Thus, projecting further surface climate of China in regional scale should not only take greenhouse gas increase into account, but also consider the impact of land use/cover types and land cover change.

Relation between Long-term Temperature and Wind Speed Trends at Surface Observation Stations in Japan

In order to detect microscale influences on observed surface air temperature changes, 29 years’ data on the AMeDAS network were used to identify the relation between temperature trends and wind speed trends, which were regarded as reflecting the changes of site exposure. The results were found to differ according to time of the day and the mean wind speed of the site. For stations with relatively high mean wind speed (≥ 1.5 m s-1), a significant negative correlation is found between daytime temperature trends and wind speed trends. For stations with low wind speed (< 1.0 m s-1), evening temperature trends are negatively correlated with wind speed trends, whereas daytime temperature trends tend to be positively correlated with them. These facts give statistical evidence of the dependence of temperature trends on microscale environmental changes, with complications according to the degree of site exposure.

Diurnal-to-seasonal characteristics of surface energy balance and temperature in East Asian summer monsoon simulations

We used regional climate model simulations of the East Asian Summer Monsoon (EASM) to study diurnal-to-seasonal characteristics of the surface energy balance and temperature, and their relationships to clouds and surface soil moisture. Simulations were performed for the period May through September 1988 and 1989, 2 years with significant differences in observed surface air temperature, radiation, and clouds over the Yangtze-Huai River Valley (YHRV), which are realistically reproduced by the model. Differences in daytime evolutions of the YHRV surface net radiation, heat fluxes, and surface air temperature between 2-year summers result mainly from fluctuation of shortwave cloud radiative forcing. Clouds dominate daily variability of the YHRV surface net solar radiation, surface net radiation, and latent heat, generally accounting for above 80% of the total variances. Consequently, they are more effective in damping the diurnal temperature range by reducing daytime maximum temperature as compared to surface soil moisture. Both clouds and soil moisture exhibit small effects on nighttime minimum temperature, which is largely controlled by the greenhouse effect of the atmospheric water vapor. Surface energy balance components and temperature evolve with the march of EASM, which is quite different between 2 years. The comparative analyses of observations and other available data can generally validate the major conclusions from the simulations.

Validation of the Surface Air Temperature Products Retrieved From the Atmospheric Infrared Sounder Over China

The Atmospheric Infrared Sounder (AIRS) is the first of a new generation of high spectral resolution atmospheric sounders, which are expected to obtain atmospheric temperature and water vapor profiles with high accuracy. We are interested in investigating the validation of AIRS surface air temperature retrievals, particularly in the region of China. The surface air temperature observations obtained from 540 ground meteorological stations over China were collected, and quantitative comparisons were performed between the AIRS Version 4 retrievals and the ground observations. Then, the main causes of retrieval errors are discussed in detail. Results show that the rms errors of the AIRS surface air temperature retrievals are correlated with the terrain altitudes of the meteorological stations. With the altitude increasing, the rms errors have a trend of gradual increase. The rms errors are insensitive to the ground-observed cloud fraction. With the observed cloud fraction increasing, the small-scale oscillations of rms errors occur. In mountainous and desert regions, the rms errors are larger and can reach up to 11 K sometimes. Furthermore, the AIRS surface air temperature retrievals have better performance in January than in July. In central and eastern China, even the accuracy of accepted quality products in January approaches the goal of the AIRS Team.

Urbanization Effects on Observed Surface Air Temperature Trends in North China

Abstract A dataset of 282 meteorological stations including all of the ordinary and national basic/reference surface stations of north China is used to analyze the urbanization effect on surface air temperature trends. These stations are classified into rural, small city, medium city, large city, and metropolis based on the updated information of total population and specific station locations. The significance of urban warming effects on regional average temperature trends is estimated using monthly mean temperature series of the station group datasets, which undergo inhomogeneity adjustment. The authors found that the largest effect of urbanization on annual mean surface air temperature trends occurs for the large-city station group, with the urban warming being 0.16°C (10 yr)−1, and the effect is the smallest for the small-city station group with urban warming being only 0.07°C (10 yr)−1. A similar assessment is made for the dataset of national basic/reference stations, which has been widely used in regional climate change analyses in China. The results indicate that the regional average annual mean temperature series, as calculated using the data from the national basic/reference stations, is significantly impacted by urban warming, and the trend of urban warming is estimated to be 0.11°C (10 yr)−1. The contribution of urban warming to total annual mean surface air temperature change as estimated with the national basic/reference station dataset reaches 37.9%. It is therefore obvious that, in the current regional average surface air temperature series in north China, or probably in the country as a whole, there still remain large effects from urban warming. The urban warming bias for the regional average temperature anomaly series is corrected. After that, the increasing rate of the regional annual mean temperature is brought down from 0.29°C (10 yr)−1 to 0.18°C (10 yr)−1, and the total change in temperature approaches 0.72°C for the period analyzed.

A global monthly land surface air temperature analysis for 1948–present

A station observation‐based global land monthly mean surface air temperature dataset at 0.5 × 0.5 latitude‐longitude resolution for the period from 1948 to the present was developed recently at the Climate Prediction Center, National Centers for Environmental Prediction. This data set is different from some existing surface air temperature data sets in: (1) using a combination of two large individual data sets of station observations collected from the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN + CAMS), so it can be regularly updated in near real time with plenty of stations and (2) some unique interpolation methods, such as the anomaly interpolation approach with spatially‐temporally varying temperature lapse rates derived from the observation‐based Reanalysis for topographic adjustment. When compared with several existing observation‐based land surface air temperature data sets, the preliminary results show that the quality of this new GHCN + CAMS land surface air temperature analysis is reasonably good and the new data set can capture most common temporal‐spatial features in the observed climatology and anomaly fields over both regional and global domains. The study also reveals that there are clear biases between the observed surface air temperature and the existing Reanalysis data sets, and they vary in space and seasons. Therefore the Reanalysis 2 m temperature data sets may not be suitable for model forcing and validation. The GHCN + CAMS data set will be mainly used as one of land surface meteorological forcing inputs to derive other land surface variables, such as soil moisture, evaporation, surface runoff, snow accumulation and snow melt, etc. As a byproduct, this monthly mean surface air temperature data set can also be applied to monitor surface air temperature variations over global land routinely or to verify the performance of model simulation and prediction.

Hierarchical evaluation of IPCC AR4 coupled climate models with systematic consideration of model uncertainties

The capability of reproducing observed surface air temperature (SAT) changes for the twentieth century is assessed using 22 multi-models which contribute to the Intergovernmental Panel on Climate Change Fourth Assessment Report. A Bayesian method is utilized for model evaluation by which model uncertainties are considered systematically. We provide a hierarchical analysis for global to sub-continental regions with two settings. First, regions of different size are evaluated separately at global, hemispheric, continental, and sub-continental scales. Second, the global SAT trend patterns are evaluated with gradual refinement of horizontal scales (higher dimensional analysis). Results show that models with natural plus anthropogenic forcing (MME_ALL) generally exhibit better skill than models with anthropogenic only forcing (MME_ANTH) at all spatial scales for different trend periods (entire twentieth century and its first and second halves). This confirms previous studies that suggest the important role of natural forcing. For the second half of the century, we found that MME_ANTH performs well compared to MME_ALL except for a few models with overestimated warming. This indicates not only major contributions of anthropogenic forcing over that period but also the applicability of both MMEs to observationally-constrained future predictions of climate changes. In addition, the skill-weighted averages with the Bayes factors [Bayesian model averaging (BMA)] show a general superiority over other error-based weighted averaging methods, suggesting a potential advantage of BMA for climate change predictions.

Seasonal Variations in Diurnal Temperature Range From Satellites and Surface Observations

The diurnal temperature range (DTR) is an important climate-change variable and, until recently, it was derived from station observations of surface air temperature (Ta). Station-based observations are sparse and unevenly distributed, making the use of satellites an attractive option for evaluating DTR. In this study, satellite-based estimates of DTR are evaluated against ground measurements of surface skin temperature (Ts) and compared with weather-station observations based on Ta. Geographical and seasonal differences were identified in both ground- and satellite-derived DTRs. Estimates of DTR from station-observed air temperature represent all-sky conditions while satellite estimates of DTR from surface skin temperature represent clear conditions only. For both station observations and satellite estimates, DTRs at rural locations tend to be larger than at urban sites. The DTRs based on Ts are larger than those derived from Ta under both all and clear-sky conditions. Clouds tend to reduce the magnitude of the DTR. The station-observed DTRs are found to be larger in summer than in winter over the entire U.S. The satellite-derived DTRs are larger in spring and fall than in winter and summer over the eastern U.S., while they are larger in spring and summer than in fall and winter over the western part. Evapotranspiration from land vegetation and the effects of water-vapor radiative forcing have a major effect on the detected spatial and seasonal variations in the DTR patterns

Climate model fidelity and projections of climate change

Relative entropy, which is a measure of the difference between two probability distributions, has been calculated for the simulations of the climate of the 20th century from 13 climate models and the observed surface air temperature during the past 100 years. This quantity is used as a measure of model fidelity: a small value of relative entropy indicates that a given model's distribution is close to the observed. It is found that there is an inverse relationship between relative entropy and the sensitivity of the model to doubling of the concentration of CO2. The models that have lower values of relative entropy, hence have higher fidelity in simulating the present climate, produce higher values of global warming for a doubling of CO2. This suggests that the projected global warming due to increasing CO2 is likely to be closer to the highest projected estimates among the current generation of climate models.

Snow effect on North American ground temperatures, 1950–2002

Changes in snow's influence on surface ground temperature (SGT) could create a bias in the borehole temperature record of climate change. Using a snow‐ground thermal model which predicts changes in the mean annual offset between SGT and surface air temperature (SAT), we calculate the response of SGT to changes in seasonal snow cover in North America from 1950 to 2002, the period for which comprehensive snow and air observations exist across the region. Daily snow and SAT observations come from the U.S. Historical Climatology Network, the Canadian Daily Climatic Dataset, and a set of National Weather Service cooperative stations in Alaska. For the period 1961–1990 the mean snow onset date in North America is 15 December, with mean snow cover duration of 81 days. There are no significant trends in either onset or duration from 1950 to 2002. Winter season air temperature, however, has warmed during this period, particularly from 1970 to 2002. The effect of the combination of a relatively stationary snow season with winter season SAT warming has been to diminish the mean annual SGT‐SAT offset by −0.05 K/decade over the past 30 years. This effect is most pronounced between 50° and 75°N in west central North America, coincident with the location of greatest winter season warming since 1970. Although comprehensive snow cover data do not exist prior to 1950, this analysis quantifies the changes in snow cover required to account for the difference between borehole temperature and multiproxy climate reconstructions.

On the USCRN Temperature System

Abstract In 2004 a new aspirated surface air temperature system was officially deployed nationally in the U.S. Climate Reference Network (USCRN) commissioned by the National Oceanic and Atmospheric Administration. The primary goal of the USCRN is to provide future long-term and high-quality homogeneous observations of surface air temperature and precipitation that can be coupled to past long-term observations for the detection and attribution of present and future climate change. In this paper two precision air temperature systems are included for evaluating the new USCRN air temperature system based on a 1-yr side-by-side field comparison. The measurement errors of the USCRN temperature sensor are systematically analyzed, and the components of error attributable to the datalogger, lead wires, fixed resistors, and the temperature coefficient of the resistors are presented. Although the current configuration is adequate, a more desirable configuration of USCRN temperature sensor coupled with the datalogger is proposed as a means of further reducing the uncertainty for the USCRN temperature measurement.

Arctic climate change: observed and modelled temperature and sea-ice variability

Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic’s response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of centuryand multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere‐ice‐ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased ∼8 × 10 5 km 2 (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.

Assessing “global warming” with surface heat content

Although climate change and variability involves all aspects of the climate system [Pielke, 1998],the assessment of anthropogenically‐forced climate change has focused on surface temperature as the primary metric [Mann and Jones, 2003; Soon et al., 2004]. Our contribution only addresses this very specific (and limited) metric of the climate system. The term “global warming” has been used to describe the observed surface air temperature increase in the 20th century. However, this concept of “global warming” requires assessments of units of heat (that is, Joules). Temperature, by itself, is an incomplete characterization of surface air heat content.