A simple, coherent framework for partitioning uncertainty in climate predictions

A simple and coherent framework for partitioning uncertainty in multi-model climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model-scenario interaction - the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the 21st century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multi-model ensembles. For example, three models are shown diverging pattern over the 21st century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

Urban microclimate and renewable energy use in cities

This paper presents an experimental measurement campaign of urban microclimate for a building complex located in London, the United Kingdom. The experiment was carried out between 19 July and 16 August, 2010 at the Elephant & Castle site. The wind and solar energy distributions within the London urban experimental site were assessed in detail for their potential use in areas of high-rise urban building complexes. The climatic variables were measured at every five minutes for the air temperature, the wind speed and direction, the air humidity and the global solar radiation for a period of four weeks. The surface temperatures were also measured on the asphalt road, pavement and building walls at every hour for the first week of the campaign period. The effect of the building complex on the urban microclimate has been analyzed in terms of the solar radiation, the air temperature and velocity. The information and observation obtained from this campaign will be useful to the analysis of renewable energy implementations in dense urban situations.

Lag-time effects on a naturally ventilated large thermometer screen

Systematic natural ventilation effects on measured temperatures within a standard large wooden thermometer screen are investigated under summer conditions, using well-calibrated platinum resistance thermometers. Under low ventilation (2mwind speed u2 < 1.1 m s−1), the screen slightly underestimates daytime air temperature but overestimates air temperature nocturnally by 0.2◦C. The screen’s lag time L lengthens with decreasing wind speed, following an inverse power law relationship between L and u2. For u2 > 2 m s−1, L ∼ 2.5 min, increasing, when calm, to at least 15 min. Spectral response properties of the screen to air temperature fluctuations vary with wind speed because of the lag changes. Ventilation effects are particularly apparent at the higher (>25◦C) temperatures, both through the lag effect and from solar heating. For sites where wind speed decreases with increasing daytime temperature, thermometer screen temperatures may consequently show larger uncertainties at the higher temperatures. Under strong direct beam solar radiation (>850W m−2) the radiation effect is likely to be <0.4◦C. Copyright c 2011 RoyalMeteorological Society

Comparing the UK Met Office climate prediction system DePreSys with idealized predictability in the HadCM3 model

The initial condition effect on climate prediction skill over a 2-year hindcast time-scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 1990–2001, and making comparisons with runs without initialization (equivalent to climatological conditions), and to anomaly persistence. It is shown that the assimilation improves the prediction skill in the first year globally, and in a number of limited areas out into the second year. Skill in hindcasting surface air temperature anomalies is most marked over ocean areas, and is coincident with areas of high sea surface temperature and ocean heat content skill. Skill improvement over land areas is much more limited but is still detectable in some cases. We found little difference in the skill of hindcasts using three different sets of ocean initial conditions, and we obtained the best results by combining these to form a grand ensemble hindcast set. Results are also compared with the idealized predictability studies of Collins (Clim. Dynam. 2002; 19: 671–692), which used the same model. The maximum lead time for which initialization gives enhanced skill over runs without initialization varies in different regions but is very similar to lead times found in the idealized studies, therefore strongly supporting the process representation in the model as well as its use for operational predictions. The limited 12-year period of the study, however, means that the regional details of model skill should probably be further assessed under a wider range of observational conditions.

The evaluation of CloudSat and CALIPSO ice microphysical products using ground-based cloud radar and lidar observations

In this paper, the statistical properties of tropical ice clouds (ice water content, visible extinction, effective radius, and total number concentration) derived from 3 yr of ground-based radar–lidar retrievals from the U.S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility in Darwin, Australia, are compared with the same properties derived using the official CloudSat microphysical retrieval methods and from a simpler statistical method using radar reflectivity and air temperature. It is shown that the two official CloudSat microphysical products (2B-CWC-RO and 2B-CWC-RVOD) are statistically virtually identical. The comparison with the ground-based radar–lidar retrievals shows that all satellite methods produce ice water contents and extinctions in a much narrower range than the ground-based method and overestimate the mean vertical profiles of microphysical parameters below 10-km height by over a factor of 2. Better agreements are obtained above 10-km height. Ways to improve these estimates are suggested in this study. Effective radii retrievals from the standard CloudSat algorithms are characterized by a large positive bias of 8–12 μm. A sensitivity test shows that in response to such a bias the cloud longwave forcing is increased from 44.6 to 46.9 W m−2 (implying an error of about 5%), whereas the negative cloud shortwave forcing is increased from −81.6 to −82.8 W m−2. Further analysis reveals that these modest effects (although not insignificant) can be much larger for optically thick clouds. The statistical method using CloudSat reflectivities and air temperature was found to produce inaccurate mean vertical profiles and probability distribution functions of effective radius. This study also shows that the retrieval of the total number concentration needs to be improved in the official CloudSat microphysical methods prior to a quantitative use for the characterization of tropical ice clouds. Finally, the statistical relationship used to produce ice water content from extinction and air temperature obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is evaluated for tropical ice clouds. It is suggested that the CALIPSO ice water content retrieval is robust for tropical ice clouds, but that the temperature dependence of the statistical relationship used should be slightly refined to better reproduce the radar–lidar retrievals.

Time of emergence of climate signals

The time at which the signal of climate change emerges from the noise of natural climate variability (Time of Emergence, ToE) is a key variable for climate predictions and risk assessments. Here we present a methodology for estimating ToE for individual climate models, and use it to make maps of ToE for surface air temperature (SAT) based on the CMIP3 global climate models. Consistent with previous studies we show that the median ToE occurs several decades sooner in low latitudes, particularly in boreal summer, than in mid-latitudes. We also show that the median ToE in the Arctic occurs sooner in boreal winter than in boreal summer. A key new aspect of our study is that we quantify the uncertainty in ToE that arises not only from inter-model differences in the magnitude of the climate change signal, but also from large differences in the simulation of natural climate variability. The uncertainty in ToE is at least 30 years in the regions examined, and as much as 60 years in some regions. Alternative emissions scenarios lead to changes in both the median ToE (by a decade or more) and its uncertainty. The SRES B1 scenario is associated with a very large uncertainty in ToE in some regions. Our findings have important implications for climate modelling and climate policy which we discuss.

Field study on thermal comfort and adaptive strategies in residential buildings in China

This paper fully describes a nation-wide field study on building thermal environment and thermal comfort of occupant, which was carried out in summer 2005 and in winter 2006 respectively in China, illustrating the adaptive strategies adopted by occupants in domestic buildings in China. According to the climate division in China, the buildings in Beijing (BJ), Shanghai (SH), Wuhan (WH) and Chongqing (CQ), Guangzhou (GZ), Kunming (KM), were selected as targets which are corresponding to cold zone, hot summer and cold winter zone (SWC-SH, WH, CQ), hot summer and warm winter zone and temperate zone, respectively. The methodology used in the field study is the combination of subjective questionnaire regarding thermal sensation and adaptive approaches and physical environmental monitoring including indoor air temperature and relative humidity. A total of 1671 subjects participate in this investigation with more than 80% response rate in all surveyed cities. Both physiological and non-physiological factors (behavioural and psychological adaptations) have been analysed.

Past, present and future change in the Atlantic meridional overturning circulation

Observations and numerical modelling experiments provide evidence for links between variability in the Atlantic Meridional Overturning Circulation (AMOC) and global climate patterns. Reduction in the strength of the overturning circulation is thought to have played a key role in rapid climate change in the past and may have the potential to significantly influence climate change in the future, as noted in the last two IPCC assessment reports (2001, 2007). Both IPCC reports also highlighted the significant uncertainties that exist regarding the future behaviour of the AMOC under global warming. Model results suggest that changes in the AMOC can impact surface air temperature, precipitation patterns and sea level, particularly in areas bordering the North Atlantic, thus affecting human populations. Here current understanding of past, present and future change in the AMOC and the effects of such changes on climate are reviewed. The focus is on observations of the AMOC, how the AMOC influences climate and in what way the AMOC is likely to change over the next few decades and the 21st 34 century. The potential for decadal prediction of the AMOC is also discussed. Finally, the outstanding challenges and possible future directions for AMOC research are outlined.

Strengthening the link between climate, hydrological and species distribution modeling to assess the impacts of climate change on freshwater biodiversity

To understand the resilience of aquatic ecosystems to environmental change, it is important to determine how multiple, related environmental factors, such as near-surface air temperature and river flow, will change during the next century. This study develops a novel methodology that combines statistical downscaling and fish species distribution modeling, to enhance the understanding of how global climate changes (modeled by global climate models at coarse-resolution) may affect local riverine fish diversity. The novelty of this work is the downscaling framework developed to provide suitable future projections of fish habitat descriptors, focusing particularly on the hydrology which has been rarely considered in previous studies. The proposed modeling framework was developed and tested in a major European system, the Adour-Garonne river basin (SW France, 116,000 km(2)), which covers distinct hydrological and thermal regions from the Pyrenees to the Atlantic coast. The simulations suggest that, by 2100, the mean annual stream flow is projected to decrease by approximately 15% and temperature to increase by approximately 1.2 °C, on average. As consequence, the majority of cool- and warm-water fish species is projected to expand their geographical range within the basin while the few cold-water species will experience a reduction in their distribution. The limitations and potential benefits of the proposed modeling approach are discussed. Copyright © 2012 Elsevier B.V. All rights reserved.

Evaluation of water and energy budgets in regional climate models applied over Europe

This study presents a model intercomparison of four regional climate models (RCMs) and one variable resolution atmospheric general circulation model (AGCM) applied over Europe with special focus on the hydrological cycle and the surface energy budget. The models simulated the 15 years from 1979 to 1993 by using quasi-observed boundary conditions derived from ECMWF re-analyses (ERA). The model intercomparison focuses on two large atchments representing two different climate conditions covering two areas of major research interest within Europe. The first is the Danube catchment which represents a continental climate dominated by advection from the surrounding land areas. It is used to analyse the common model error of a too dry and too warm simulation of the summertime climate of southeastern Europe. This summer warming and drying problem is seen in many RCMs, and to a less extent in GCMs. The second area is the Baltic Sea catchment which represents maritime climate dominated by advection from the ocean and from the Baltic Sea. This catchment is a research area of many studies within Europe and also covered by the BALTEX program. The observed data used are monthly mean surface air temperature, precipitation and river discharge. For all models, these are used to estimate mean monthly biases of all components of the hydrological cycle over land. In addition, the mean monthly deviations of the surface energy fluxes from ERA data are computed. Atmospheric moisture fluxes from ERA are compared with those of one model to provide an independent estimate of the convergence bias derived from the observed data. These help to add weight to some of the inferred estimates and explain some of the discrepancies between them. An evaluation of these biases and deviations suggests possible sources of error in each of the models. For the Danube catchment, systematic errors in the dynamics cause the prominent summer drying problem for three of the RCMs, while for the fourth RCM this is related to deficiencies in the land surface parametrization. The AGCM does not show this drying problem. For the Baltic Sea catchment, all models similarily overestimate the precipitation throughout the year except during the summer. This model deficit is probably caused by the internal model parametrizations, such as the large-scale condensation and the convection schemes.

Estimating natural-ventilation potential considering both thermal comfort and IAQ issues

Natural-ventilation potential (NVP) value can provide the designers significant information to properly design and arrange natural ventilation strategy at the preliminary or conceptual stage of ventilation and building design. Based on the previous study by Yang et al. [Investigation potential of natural driving forces for ventilation in four major cities in China. Building and Environment 2005;40:739–46], we developed a revised model to estimate the potential for natural ventilation considering both thermal comfort and IAQ issues for buildings in China. It differs from the previous one by Yang et al. in two predominant aspects: (1) indoor air temperature varies synchronously with the outdoor air temperature rather than staying at a constant value as assumed by Yang et al. This would recover the real characteristic of natural ventilation, (2) thermal comfort evaluation index is integrated into the model and thus the NVP can be more reasonably predicted. By adopting the same input parameters, the NVP values are obtained and compared with the early work of Yang et al. for a single building in four representative cities which are located in different climates, i.e., Urumqi in severe cold regions, Beijing in cold regions, Shanghai in hot summer and cold winter regions and Guangzhou in hot summer and warm winter regions of China. Our outcome shows that Guangzhou has the highest and best yearly natural-ventilation potential, followed by Shanghai, Beijing and Urumqi, which is quite distinct from that of Yang et al. From the analysis, it is clear that our model evaluates the NVP values more consistently with the outdoor climate data and thus reveals the true value of NVP.

Initialisation and predictability of the AMOC over the last 50 years in a climate model

The mechanisms involved in Atlantic meridional overturning circulation (AMOC) decadal variability and predictability over the last 50 years are analysed in the IPSL–CM5A–LR model using historical and initialised simulations. The initialisation procedure only uses nudging towards sea surface temperature anomalies with a physically based restoring coefficient. When compared to two independent AMOC reconstructions, both the historical and nudged ensemble simulations exhibit skill at reproducing AMOC variations from 1977 onwards, and in particular two maxima occurring respectively around 1978 and 1997. We argue that one source of skill is related to the large Mount Agung volcanic eruption starting in 1963, which reset an internal 20-year variability cycle in the North Atlantic in the model. This cycle involves the East Greenland Current intensity, and advection of active tracers along the subpolar gyre, which leads to an AMOC maximum around 15 years after the Mount Agung eruption. The 1997 maximum occurs approximately 20 years after the former one. The nudged simulations better reproduce this second maximum than the historical simulations. This is due to the initialisation of a cooling of the convection sites in the 1980s under the effect of a persistent North Atlantic oscillation (NAO) positive phase, a feature not captured in the historical simulations. Hence we argue that the 20-year cycle excited by the 1963 Mount Agung eruption together with the NAO forcing both contributed to the 1990s AMOC maximum. These results support the existence of a 20-year cycle in the North Atlantic in the observations. Hindcasts following the CMIP5 protocol are launched from a nudged simulation every 5 years for the 1960–2005 period. They exhibit significant correlation skill score as compared to an independent reconstruction of the AMOC from 4-year lead-time average. This encouraging result is accompanied by increased correlation skills in reproducing the observed 2-m air temperature in the bordering regions of the North Atlantic as compared to non-initialized simulations. To a lesser extent, predicted precipitation tends to correlate with the nudged simulation in the tropical Atlantic. We argue that this skill is due to the initialisation and predictability of the AMOC in the present prediction system. The mechanisms evidenced here support the idea of volcanic eruptions as a pacemaker for internal variability of the AMOC. Together with the existence of a 20-year cycle in the North Atlantic they propose a novel and complementary explanation for the AMOC variations over the last 50 years.

Decadal to multidecadal variability and the climate change background

Three prominent quasi-global patterns of variability and change are observed using the Met Office's sea surface temperature (SST) analysis and almost independent night marine air temperature analysis. The first is a global warming signal that is very highly correlated with global mean SST. The second is a decadal to multidecadal fluctuation with some geographical similarity to the El Niño–Southern Oscillation (ENSO). It is associated with the Pacific Decadal Oscillation (PDO), and its Pacific-wide manifestation has been termed the Interdecadal Pacific Oscillation (IPO). We present model investigations of the relationship between the IPO and ENSO. The third mode is an interhemispheric variation on multidecadal timescales which, in view of climate model experiments, is likely to be at least partly due to natural variations in the thermohaline circulation. Observed climatic impacts of this mode also appear in model simulations. Smaller-scale, regional atmospheric phenomena also affect climate on decadal to interdecadal timescales. We concentrate on one such mode, the winter North Atlantic Oscillation (NAO). This shows strong decadal to interdecadal variability and a correspondingly strong influence on surface climate variability which is largely additional to the effects of recent regional anthropogenic climate change. The winter NAO is likely influenced by both SST forcing and stratospheric variability. A full understanding of decadal changes in the NAO and European winter climate may require a detailed representation of the stratosphere that is hitherto missing in the major climate models used to study climate change.

Liquid-vapour homogenisation of fluid inclusions in stalagmites: Evaluation of a new thermometer for paleoclimate research

We present a new approach to determine palaeotemperatures (mean annual surface temperatures) based on measurements of the liquid–vapour homogenisation temperature of fluid inclusions in stalagmites. The aim of this study is to explore the potential and the limitations of this new palaeothermometer and to develop a reliable methodology for routine applications in palaeoclimate research. Therefore, we have investigated recent fluid inclusions from the top part of actively growing stalagmites that have formed at temperatures close to the present-day cave air temperature. A precondition for measuring homogenisation temperatures of originally monophase inclusions is the nucleation of a vapour bubble by means of single ultra-short laser pulses. Based on the observed homogenisation temperatures (Th(obs)) and measurements of the vapour bubble diameter at a known temperature, we calculated stalagmite formation temperatures (Tf) by applying a thermodynamic model that takes into account the effect of surface tension on liquid–vapour homogenisation. Results from recent stalagmite samples demonstrate that calculated stalagmite formation temperatures match the present-day cave air temperature within ± 0.2 °C. To avoid artificially induced changes of the fluid density we defined specific demands on the selection, handling and preparation of the stalagmite samples. Application of the method is restricted to stalagmites that formed at cave temperatures greater than ~ 9–11 °C.

Evaluation of water and energy budgets in regional climate models applied over Europe

Abstract This study presents a model intercomparison of four regional climate models (RCMs) and one variable resolution atmospheric general circulation model (AGCM) applied over Europe with special focus on the hydrological cycle and the surface energy budget. The models simulated the 15 years from 1979 to 1993 by using quasi-observed boundary conditions derived from ECMWF re-analyses (ERA). The model intercomparison focuses on two large atchments representing two different climate conditions covering two areas of major research interest within Europe. The first is the Danube catchment which represents a continental climate dominated by advection from the surrounding land areas. It is used to analyse the common model error of a too dry and too warm simulation of the summertime climate of southeastern Europe. This summer warming and drying problem is seen in many RCMs, and to a less extent in GCMs. The second area is the Baltic Sea catchment which represents maritime climate dominated by advection from the ocean and from the Baltic Sea. This catchment is a research area of many studies within Europe and also covered by the BALTEX program. The observed data used are monthly mean surface air temperature, precipitation and river discharge. For all models, these are used to estimate mean monthly biases of all components of the hydrological cycle over land. In addition, the mean monthly deviations of the surface energy fluxes from ERA data are computed. Atmospheric moisture fluxes from ERA are compared with those of one model to provide an independent estimate of the convergence bias derived from the observed data. These help to add weight to some of the inferred estimates and explain some of the discrepancies between them. An evaluation of these biases and deviations suggests possible sources of error in each of the models. For the Danube catchment, systematic errors in the dynamics cause the prominent summer drying problem for three of the RCMs, while for the fourth RCM this is related to deficiencies in the land surface parametrization. The AGCM does not show this drying problem. For the Baltic Sea catchment, all models similarily overestimate the precipitation throughout the year except during the summer. This model deficit is probably caused by the internal model parametrizations, such as the large-scale condensation and the convection schemes.