Equivalent-barotropic definition of tropospheric mean temperature

An equivalent-barotropic (EB) description of the tropospheric temperature field is derived from the geostrophic empirical mode (GEM) in the form of a scalar function Gamma(p, phi), where p is pressure and phi is 300-850-mb thickness. Baroclinic parameter phi plays the role of latitude at each longitudinal section. Compared with traditional Eulerian-mean methods, GEM defines a mean field in baroclinic streamfunction space with a time scale much longer than synoptic variability. It prompts an EB concept that is only based on a baroclinic field. Monthly GEM fields are diagnosed from NCEP-NCAR reanalysis data and account for more than 90% of the tropospheric thermal variance. The circumglobal composite of GEM fields exhibits seasonal, zonal, and hemispheric asymmetries, with larger rms errors occurring in winter and in the Northern Hemisphere (NH). Zonally asymmetric features and planetary deviation from EB are seen in the NH winter GEM. Reconstruction of synoptic sections and correlation analysis reveal that the tropospheric temperature field is EB at the leading order and has a 1-day phase lag behind barotropic variations in extratropical regions.

An evaluation of the accuracy of models for the determination of the weighted mean temperature of the atmosphere

The estimates of the zenith wet delay resulting from the analysis of data from space techniques, such as GPS and VLBI, have a strong potential in climate modeling and weather forecast applications. In order to be useful to meteorology, these estimates have to be converted to precipitable water vapor, a process that requires the knowledge of the weighted mean temperature of the atmosphere, which varies both in space and time. In recent years, several models have been proposed to predict this quantity. Using a database of mean temperature values obtained by ray-tracing radiosonde profiles of more than 100 stations covering the globe, and about 2.5 year’s worth of data, we have analyzed several of these models. Based on data from the European region, we have concluded that the models provide identical levels of precision, but different levels of accuracy. Our results indicate that regionally-optimized models do not provide superior performance compared to the global models.

Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10–90th percentile ranges of 1.7 K for the aggressive mitigation scenario, up to 3.9 K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climate-sensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work.

What does global mean temperature tell us about local climate?

The subject of climate feedbacks focuses attention on global mean surface air temperature (GMST) as the key metric of climate change. But what does knowledge of past and future GMST tell us about the climate of specific regions? In the context of the ongoing UNFCCC process, this is an important question for policy-makers as well as for scientists. The answer depends on many factors, including the mechanisms causing changes, the timescale of the changes, and the variables and regions of interest. This paper provides a review and analysis of the relationship between changes in GMST and changes in local climate, first in observational records and then in a range of climate model simulations, which are used to interpret the observations. The focus is on decadal timescales, which are of particular interest in relation to recent and near-future anthropogenic climate change. It is shown that GMST primarily provides information about forced responses, but that understanding and quantifying internal variability is essential to projecting climate and climate impacts on regional-to-local scales. The relationship between local forced responses and GMST is often linear but may be nonlinear, and can be greatly complicated by competition between different forcing factors. Climate projections are limited not only by uncertainties in the signal of climate change but also by uncertainties in the characteristics of real-world internal variability. Finally, it is shown that the relationship between GMST and local climate provides a simple approach to climate change detection, and a useful guide to attribution studies.

Estimativa do vapor d'água atmosférico e avaliação da modelagem do atraso zenital troposférico utilizando GPS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimativas do IWV utilizando receptores GPS em bases terrestres no Brasil: sinergia entre a geodésia e a meteorologia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The response of tropospheric circulation to perturbations in lower-stratospheric temperature

A multiple regression analysis of the NCEP-NCAR reanalysis dataset shows a response to increased solar activity of a weakening and poleward shift of the subtropical jets. This signal is separable from other influences, such as those of El Nino-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), and is very similar to that seen in previous studies using global circulation models (GCMs) of the effects of an increase in solar spectral irradiance. The response to increased stratospheric (volcanic) aerosol is found in the data to be a weakening and equatorward shift of the jets. The GCM studies of the solar influence also showed an impact on tropospheric mean meridional circulation with a weakening and expansion of the tropical Hadley cells and a poleward shift of the Ferrel cells. To understand the mechanisms whereby the changes in solar irradiance affect tropospheric winds and circulation, experiments have been carried out with a simplified global circulation model. The results show that generic heating of the lower stratosphere tends to weaken the subtropical jets and the tropospheric mean meridional circulations. The positions of the jets, and the extent of the Hadley cells, respond to the distribution of the stratospheric heating, with low-latitude heating forcing them to move poleward, and high-latitude or latitudinally uniform heating forcing them equatorward. The patterns of response are similar to those that are found to be a result of the solar or volcanic influences, respectively, in the data analysis. This demonstrates that perturbations to the heat balance of the lower stratosphere, such as those brought about by solar or volcanic activity, can produce changes in the mean tropospheric circulation, even without any direct forcing below the tropopause.

Surface water temperature of Windermere. Monthly and yearly totals of degree-days centigrade and monthly mean temperatures, 1933 to 1975

The surface temperature of Windermere has been recorded by the staff of the Freshwater Biological Association on every weekday (with a few minor exceptions) since 11 January 1933. This publication presents this information in a form which can easily be used by individual research workers. There are 43 tables (1 for each year, 1933-1975) which give the data, expressed as degree-days centigrade. The tables show for each month the number of degree-days above each temperature from 0 degree C to the highest recorded, at 1 degree C intervals. Mean temperatures are obtained by dividing the number of degree-days over 0 degree C by the relevant number of days. The advantage of degree-days rather than mean temperatures is that degree-days are additive so data for any desired periods may be combined quickly and simply. Seasonal results for spring, summer, autumn and winter are presented in tabular form, as are selected totals for comparisons between years. Further tables give the mean temperature in each month of the year, and the frequency distributions of monthly mean temperatures.

Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change

Previous assessments of the impacts of climate change on heat-related mortality use the "delta method" to create temperature projection time series that are applied to temperature-mortality models to estimate future mortality impacts. The delta method means that climate model bias in the modelled present does not influence the temperature projection time series and impacts. However, the delta method assumes that climate change will result only in a change in the mean temperature but there is evidence that there will also be changes in the variability of temperature with climate change. The aim of this paper is to demonstrate the importance of considering changes in temperature variability with climate change in impacts assessments of future heat-related mortality. We investigate future heatrelated mortality impacts in six cities (Boston, Budapest, Dallas, Lisbon, London and Sydney) by applying temperature projections from the UK Meteorological Office HadCM3 climate model to the temperature-mortality models constructed and validated in Part 1. We investigate the impacts for four cases based on various combinations of mean and variability changes in temperature with climate change. The results demonstrate that higher mortality is attributed to increases in the mean and variability of temperature with climate change rather than with the change in mean temperature alone. This has implications for interpreting existing impacts estimates that have used the delta method. We present a novel method for the creation of temperature projection time series that includes changes in the mean and variability of temperature with climate change and is not influenced by climate model bias in the modelled present. The method should be useful for future impacts assessments. Few studies consider the implications that the limitations of the climate model may have on the heatrelated mortality impacts. Here, we demonstrate the importance of considering this by conducting an evaluation of the daily and extreme temperatures from HadCM3, which demonstrates that the estimates of future heat-related mortality for Dallas and Lisbon may be overestimated due to positive climate model bias. Likewise, estimates for Boston and London may be underestimated due to negative climate model bias. Finally, we briefly consider uncertainties in the impacts associated with greenhouse gas emissions and acclimatisation. The uncertainties in the mortality impacts due to different emissions scenarios of greenhouse gases in the future varied considerably by location. Allowing for acclimatisation to an extra 2°C in mean temperatures reduced future heat-related mortality by approximately half that of no acclimatisation in each city.

Projected impacts on heat-related mortality from changes in the mean and variability of temperature with climate change

The aim of this paper is to demonstrate the importance of changing temperature variability with climate change in assessments of future heat-related mortality. Previous studies have only considered changes in the mean temperature. Here we present estimates of heat-related mortality resulting from climate change for six cities: Boston, Budapest, Dallas, Lisbon, London and Sydney. They are based on climate change scenarios for the 2080s (2070-2099) and the temperature-mortality (t-m) models constructed and validated in Gosling et al. (2007). We propose a novel methodology for assessing the impacts of climate change on heat-related mortality that considers both changes in the mean and variability of the temperature distribution.

On the evaluation of temperature trends in the tropical troposphere

A series of model experiments with the coupled Max-Planck-Institute ECHAM5/OM climate model have been investigated and compared with microwave measurements from the Microwave Sounding Unit (MSU) and re-analysis data for the period 1979–2008. The evaluation is carried out by computing the Temperature in the Lower Troposphere (TLT) and Temperature in the Middle Troposphere (TMT) using the MSU weights from both University of Alabama (UAH) and Remote Sensing Systems (RSS) and restricting the study to primarily the tropical oceans. When forced by analysed sea surface temperature the model reproduces accurately the time-evolution of the mean outgoing tropospheric microwave radiation especially over tropical oceans but with a minor bias towards higher temperatures in the upper troposphere. The latest reanalyses data from the 25 year Japanese re-analysis (JRA25) and European Center for Medium Range Weather Forecasts Interim Reanalysis are in very close agreement with the time-evolution of the MSU data with a correlation of 0.98 and 0.96, respectively. The re-analysis trends are similar to the trends obtained from UAH but smaller than the trends from RSS. Comparison of TLT, computed from observations from UAH and RSS, with Sea Surface Temperature indicates that RSS has a warm bias after 1993. In order to identify the significance of the tropospheric linear temperature trends we determined the natural variability of 30-year trends from a 500 year control integration of the coupled ECHAM5 model. The model exhibits natural unforced variations of the 30 year tropospheric trend that vary within ±0.2 K/decade for the tropical oceans. This general result is supported by similar results from the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model. Present MSU observations from UAH for the period 1979–2008 are well within this range but RSS is close to the upper positive limit of this variability. We have also compared the trend of the vertical lapse rate over the tropical oceans assuming that the difference between TLT and TMT is an approximate measure of the lapse rate. The TLT–TMT trend is larger in both the measurements and in the JRA25 than in the model runs by 0.04–0.06 K/decade. Furthermore, a calculation of all 30 year TLT–TMT trends of the unforced 500-year integration vary between ±0.03 K/decade suggesting that the models have a minor systematic warm bias in the upper troposphere.