Mean Near-surface Air Temperature Research Articles

Greenland ice sheet surface mass balance: evaluating simulations and making projections with regional climate models

Abstract. Four high-resolution regional climate models (RCMs) have been set up for the area of Greenland, with the aim of providing future projections of Greenland ice sheet surface mass balance (SMB), and its contribution to sea level rise, with greater accuracy than is possible from coarser-resolution general circulation models (GCMs). This is the first time an intercomparison has been carried out of RCM results for Greenland climate and SMB. Output from RCM simulations for the recent past with the four RCMs is evaluated against available observations. The evaluation highlights the importance of using a detailed snow physics scheme, especially regarding the representations of albedo and meltwater refreezing. Simulations with three of the RCMs for the 21st century using SRES scenario A1B from two GCMs produce trends of between −5.5 and −1.1 Gt yr−2 in SMB (equivalent to +0.015 and +0.003 mm sea level equivalent yr−2), with trends of smaller magnitude for scenario E1, in which emissions are mitigated. Results from one of the RCMs whose present-day simulation is most realistic indicate that an annual mean near-surface air temperature increase over Greenland of ~ 2°C would be required for the mass loss to increase such that it exceeds accumulation, thereby causing the SMB to become negative, which has been suggested as a threshold beyond which the ice sheet would eventually be eliminated.

Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing

Abstract The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to preindustrial forcing. The response is characterized by an initial fast exponential decay with an e-folding time smaller than 5 yr, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4°C by 2100 in the A1B scenario from the Special Report on Emissions Scenarios (SRES), and then to 1.4°C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO2 and by the excellent fit to the model’s ensemble mean twentieth-century evolution with a simple one-box model with no long times scales.

Granger causality and Atlantic hurricanes

Atlantic tropical cyclones have been getting stronger recently with a trend that is related to an increase in the late summer/early fall sea-surface temperature over the North Atlantic. Some studies attribute the increasing ocean warmth and hurricane intensity to a natural climate fluctuation, known as the Atlantic Multidecadal Oscillation; others suggest that climate change related to anthropogenic greenhouse gases emissions is the cause. Noting that the only difference between these two hypotheses is the causal connection between global mean near-surface air temperature (GT) and Atlantic sea-surface temperature (SST), the author previously showed how to use statistical tests to examine this hypothesis. Here the author expands on this research. In particular, a more comprehensive explanation of the techniques and additional tests and checks against misspecification are provided. The earlier results are confirmed in showing that preceding GT anomalies have a significant statistical relationship to current SST anomalies but not conversely so that if causality exists between Atlantic SST and global temperature, the causal direction likely goes from GT to SST. The result is robust against a small amount of noise added to the data. Identical tests applied to surrogate time series fail to identify causality as expected. The work underscores the importance of using data models to understand relationships between hurricanes and climate.

The regional scale impact of land cover change simulated with a climate model

AbstractA series of 17‐year integrations using the NCAR CCM3 (at about 2.8° × 2.8° resolution) were performed to investigate the regional‐scale impact of land cover change. Our aim was to determine the impact of historical land cover change on the regional‐scale climate over the regions where most change occurred: Europe, India and China. The change from natural to current land cover was estimated using BIOME3 to predict the natural vegetation type, and then using remotely sensed data to estimate the locations where land cover had been changed through human activity. Results show statistically significant changes in the 15‐year averaged 1000 hPa wind field, mean near‐surface air temperature, maximum near‐surface air temperature and the latent heat flux over the regions where land cover change was imposed. These changes disappeared if the land cover over a particular region was omitted, indicating that our results cannot be explained by model variability. An analysis of changes on an averaged monthly time scale showed large changes in the maximum daily temperature in (Northern Hemisphere) summer and little change in the minimum daily temperature, resulting in changes in the diurnal temperature range. The change in the diurnal temperature range could be positive or negative depending on region, time of year and the precise nature of the land cover changes. Our results indicate that the inclusion of land cover change scenarios in simulations of the 20th century may lead to improved results. The impact of land cover changes on regional climates also provides support for the inclusion of land surface models that can represent future land cover changes resulting from an ecological response to natural climate variability or increasing carbon dioxide. Copyright © 2002 Royal Meteorological Society.

Statistical downscaling of general-circulation-model- simulated average monthly air temperature to the beginning of flowering of the dandelion (Taraxacum officinale) in Slovenia.

Phenological observations are a valuable source of information for investigating the relationship between climate variation and plant development. Potential climate change in the future will shift the occurrence of phenological phases. Information about future climate conditions is needed in order to estimate this shift. General circulation models (GCM) provide the best information about future climate change. They are able to simulate reliably the most important mean features on a large scale, but they fail on a regional scale because of their low spatial resolution. A common approach to bridging the scale gap is statistical downscaling, which was used to relate the beginning of flowering of Taraxacum officinale in Slovenia with the monthly mean near-surface air temperature for January, February and March in Central Europe. Statistical models were developed and tested with NCAR/NCEP Reanalysis predictor data and EARS predict and data for the period 1960-1999. Prior to developing statistical models, empirical orthogonal function (EOF) analysis was employed on the predictor data. Multiple linear regression was used to relate the beginning of flowering with expansion coefficients of the first three EOF for the Janauary, Febrauary and March air temperatures, and a strong correlation was found between them. Developed statistical models were employed on the results of two GCM (HadCM3 and ECHAM4/OPYC3) to estimate the potential shifts in the beginning of flowering for the periods 1990-2019 and 2020-2049 in comparison with the period 1960-1989. The HadCM3 model predicts, on average, 4 days earlier occurrence and ECHAM4/OPYC3 5 days earlier occurrence of flowering in the period 1990-2019. The analogous results for the period 2020-2049 are a 10- and 11-day earlier occurrence.

An analysis of simulated and observed global mean near-surface air temperature anomalies from 1979 to 1999: trends and attribution of causes

The 1979–1999 response of the climate system to variations in solar spectral irradiance is estimated by comparing the global averaged surface temperature anomalies simulated by a 2-D energy balance climate model to observed temperature anomalies. We perform a multiple regression of southern oscillation index and the individual model responses to solar irradiance variations, stratospheric and tropospheric aerosol loading, stratospheric ozone trends, and greenhouse gases onto each of five near-surface temperature anomaly data sets. We estimate the observed difference in global mean near-surface air temperature attributable to the solar irradiance difference between solar maximum and solar minimum to be between 0.06 and 0.11 K, and that 1.1–3.8% of the total variance in monthly mean near-surface air temperature data is attributable to variations in solar spectral irradiance. For the five temperature data sets used in our analysis, the trends in raw monthly mean temperature anomaly data have a large range, spanning a factor of 3 from 0.06 to 0.17 K/decade. However, our analysis suggests that trends in monthly temperature anomalies attributable to the combination of greenhouse gas, stratospheric ozone, and tropospheric sulfate aerosol variations are much more consistent among data sets, ranging from 0.16 to 0.24 K/decade. Our model results suggest that roughly half of the warming from greenhouse gases is cancelled by the cooling from changes in stratospheric ozone. Tropospheric sulfate aerosol loading in the present day atmosphere contributes significantly to the net radiative forcing of the present day climate system. However, because the change in magnitude and latitudinal distribution of tropospheric sulfate aerosol has been small over the past 20 years, the change in the direct radiative forcing attributable to changes in aerosol loading over this time is also small.

Long-term climate changes due to increased CO2 concentration in the coupled atmosphere-ocean general circulation model ECHAM3/LSG

The long-term adjustment processes of atmosphere and ocean in response to gradually increased atmospheric CO2 concentration have been analysed in two 850-year integrations with a coupled atmosphere-ocean general circulation model (AOGCM). In these experiments the CO2 concentration has been increased to double and four times the initial concentration, respectively, and is kept fixed thereafter. Three characteristic time scales have been identified: a very fast response associated with processes dominated by the atmospheric adjustment, an intermediate time scale of a few decades connected with processes in the upper ocean, and adjustment processes with time scales of centuries and longer due to the inertia of the deep ocean. The latter in particular is responsible for a still ongoing adjustment of the atmosphere-ocean system at the end of the integrations after 850 years. After 60 years, at the time of CO2 doubling, the global mean near-surface air temperature rises by 1.4 K. In spite of the constant CO2 concentration during the following centuries the warming continues to 2.6 K after 850 years. The behaviour of the quadrupling run is similar: global mean near-surface air temperature increases by 3.8 K at the time of CO2 quadrupling and by 4.8 K at the end of the simulation. The thermohaline circulation undergoes remarkable changes. Temporarily, the North Atlantic overturning circulation weakens by up to 30% in the CO2 doubling experiment and up to 50% in the CO2 quadrupling experiment. After reaching the minimum the North Atlantic overturning slowly recovers in both experiments.