A method for incorporating climate variability in climate change impact assessments: sensitivity of river flows in the Eden catchment to precipitation scenarios

Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate cahgne projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment's role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability.

Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability

Systematic climate shifts have been linked to multidecadal variability in observed sea surface temperatures in the North Atlantic Ocean1. These links are extensive, influencing a range of climate processes such as hurricane activity2 and African Sahel3, 4, 5 and Amazonian5 droughts. The variability is distinct from historical global-mean temperature changes and is commonly attributed to natural ocean oscillations6, 7, 8, 9, 10. A number of studies have provided evidence that aerosols can influence long-term changes in sea surface temperatures11, 12, but climate models have so far failed to reproduce these interactions6, 9 and the role of aerosols in decadal variability remains unclear. Here we use a state-of-the-art Earth system climate model to show that aerosol emissions and periods of volcanic activity explain 76 per cent of the simulated multidecadal variance in detrended 1860–2005 North Atlantic sea surface temperatures. After 1950, simulated variability is within observational estimates; our estimates for 1910–1940 capture twice the warming of previous generation models but do not explain the entire observed trend. Other processes, such as ocean circulation, may also have contributed to variability in the early twentieth century. Mechanistically, we find that inclusion of aerosol–cloud microphysical effects, which were included in few previous multimodel ensembles, dominates the magnitude (80 per cent) and the spatial pattern of the total surface aerosol forcing in the North Atlantic. Our findings suggest that anthropogenic aerosol emissions influenced a range of societally important historical climate events such as peaks in hurricane activity and Sahel drought. Decadal-scale model predictions of regional Atlantic climate will probably be improved by incorporating aerosol–cloud microphysical interactions and estimates of future concentrations of aerosols, emissions of which are directly addressable by policy actions.

Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland

As wind generation increases, system impact studies rely on predictions of future generation and effective representation of wind variability. A well-established approach to investigate the impact of wind variability is to simulate generation using observations from 10 m meteorological mast-data. However, there are problems with relying purely on historical wind-speed records or generation histories: mast-data is often incomplete, not sited at a relevant wind generation sites, and recorded at the wrong altitude above ground (usually 10 m), each of which may distort the generation profile. A possible complimentary approach is to use reanalysis data, where data assimilation techniques are combined with state-of-the-art weather forecast models to produce complete gridded wind time-series over an area. Previous investigations of reanalysis datasets have placed an emphasis on comparing reanalysis to meteorological site records whereas this paper compares wind generation simulated using reanalysis data directly against historic wind generation records. Importantly, this comparison is conducted using raw reanalysis data (typical resolution ∼50 km), without relying on a computationally expensive “dynamical downscaling” for a particular target region. Although the raw reanalysis data cannot, by nature of its construction, represent the site-specific effects of sub-gridscale topography, it is nevertheless shown to be comparable to or better than the mast-based simulation in the region considered and it is therefore argued that raw reanalysis data may offer a number of significant advantages as a data source.

Describing seasonal variability in the distribution of daily effective temperatures for 1985-2009 compared to 1904-1984 for De Bilt, Holland

This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904–1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested.

Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part I: the representation of the MJO

As a major mode of intraseasonal variability, which interacts with weather and climate systems on a near-global scale, the Madden – Julian Oscillation (MJO) is a crucial source of predictability for numerical weather prediction (NWP) models. Despite its global significance and comprehensive investigation, improvements in the representation of the MJO in an NWP context remain elusive. However, recent modifications to the model physics in the ECMWF model led to advances in the representation of atmospheric variability and the unprecedented propagation of the MJO signal through the entire integration period. In light of these recent advances, a set of hindcast experiments have been designed to assess the sensitivity of MJO simulation to the formulation of convection. Through the application of established MJO diagnostics, it is shown that the improvements in the representation of the MJO can be directly attributed to the modified convective parametrization. Furthermore, the improvements are attributed to the move from a moisture-convergent- to a relative-humidity-dependent formulation for organized deep entrainment. It is concluded that, in order to understand the physical mechanisms through which a relative-humidity-dependent formulation for entrainment led to an improved simulation of the MJO, a more process-based approach should be taken. T he application of process-based diagnostics t o t he hindcast experiments presented here will be the focus of Part II of this study.

Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part II: the application of process-based diagnostics

In Part I of this study it was shown that moving from a moisture-convergent- to a relative-humidity-dependent organized entrainment rate in the formulation for deep convection was responsible for significant advances in the simulation of the Madden – Julian Oscillation (MJO) in the ECMWF model. However, the application of traditional MJO diagnostics were not adequate to understand why changing the control on convection had such a pronounced impact on the representation of the MJO. In this study a set of process-based diagnostics are applied to the hindcast experiments described in Part I to identify the physical mechanisms responsible for the advances in MJO simulation. Increasing the sensitivity of the deep convection scheme to environmental moisture is shown to modify the relationship between precipitation and moisture in the model. Through dry-air entrainment, convective plumes ascending in low-humidity environments terminate lower in the atmosphere. As a result, there is an increase in the occurrence of cumulus congestus, which acts to moisten the mid troposphere. Due to the modified precipitation – moisture relationship more moisture is able to build up, which effectively preconditions the tropical atmosphere for the t ransition t o d eep convection. R esults from this study suggest that a tropospheric moisture control on convection is key to simulating the interaction between the convective heating and the large-scale wave forcing associated with the MJO.

Stratospheric variability and tropospheric annular‐mode timescales

Climate models tend to exhibit much too persistent Southern Annular Mode (SAM) circulation anomalies in summer, compared to observations. Theoretical arguments suggest this bias may lead to an overly strong model response to anthropogenic forcing during this season, which is of interest since the largest observed changes in Southern Hemisphere high‐latitude climate over the last few decades have occurred in summer, and are congruent with the SAM. The origin of this model bias is examined here in the case of the Canadian Middle Atmosphere Model, using a novel technique to quantify the influence of stratospheric variability on tropospheric annular‐mode timescales. Part of the model bias is shown to be attributable to the too‐late breakdown of the stratospheric polar vortex, which allows the tropospheric influence of stratospheric variability to extend into early summer. However, the analysis also reveals an enhanced summertime persistence of the model’s SAM that is unrelated to either stratospheric variability or the bias in model stratospheric climatology, and is thus of tropospheric origin. No such feature is evident in the Northern Hemisphere. The effect of stratospheric variability in lengthening tropospheric annular‐mode timescales is evident in both hemispheres. While in the Southern Hemisphere the effect is restricted to late‐spring/early summer, in the Northern Hemisphere it can occur throughout the winter‐spring season, with the seasonality of peak timescales exhibiting considerable variability between different 50 year sections of the same simulation.

Stratosphere‐troposphere coupling and annular mode variability in chemistry‐climate models

The internal variability and coupling between the stratosphere and troposphere in CCMVal‐2 chemistry‐climate models are evaluated through analysis of the annular mode patterns of variability. Computation of the annular modes in long data sets with secular trends requires refinement of the standard definition of the annular mode, and a more robust procedure that allows for slowly varying trends is established and verified. The spatial and temporal structure of the models’ annular modes is then compared with that of reanalyses. As a whole, the models capture the key features of observed intraseasonal variability, including the sharp vertical gradients in structure between stratosphere and troposphere, the asymmetries in the seasonal cycle between the Northern and Southern hemispheres, and the coupling between the polar stratospheric vortices and tropospheric midlatitude jets. It is also found that the annular mode variability changes little in time throughout simulations of the 21st century. There are, however, both common biases and significant differences in performance in the models. In the troposphere, the annular mode in models is generally too persistent, particularly in the Southern Hemisphere summer, a bias similar to that found in CMIP3 coupled climate models. In the stratosphere, the periods of peak variance and coupling with the troposphere are delayed by about a month in both hemispheres. The relationship between increased variability of the stratosphere and increased persistence in the troposphere suggests that some tropospheric biases may be related to stratospheric biases and that a well‐simulated stratosphere can improve simulation of tropospheric intraseasonal variability.

The Early Twentieth-Century Warming in the Arctic—A Possible Mechanism

The huge warming of the Arctic that started in the early 1920s and lasted for almost two decades is one of the most spectacular climate events of the twentieth century. During the peak period 1930–40, the annually averaged temperature anomaly for the area 60°–90°N amounted to some 1.7°C. Whether this event is an example of an internal climate mode or is externally forced, such as by enhanced solar effects, is presently under debate. This study suggests that natural variability is a likely cause, with reduced sea ice cover being crucial for the warming. A robust sea ice–air temperature relationship was demonstrated by a set of four simulations with the atmospheric ECHAM model forced with observed SST and sea ice concentrations. An analysis of the spatial characteristics of the observed early twentieth-century surface air temperature anomaly revealed that it was associated with similar sea ice variations. Further investigation of the variability of Arctic surface temperature and sea ice cover was performed by analyzing data from a coupled ocean–atmosphere model. By analyzing climate anomalies in the model that are similar to those that occurred in the early twentieth century, it was found that the simulated temperature increase in the Arctic was related to enhanced wind-driven oceanic inflow into the Barents Sea with an associated sea ice retreat. The magnitude of the inflow is linked to the strength of westerlies into the Barents Sea. This study proposes a mechanism sustaining the enhanced westerly winds by a cyclonic atmospheric circulation in the Barents Sea region created by a strong surface heat flux over the ice-free areas. Observational data suggest a similar series of events during the early twentieth-century Arctic warming, including increasing westerly winds between Spitsbergen and Norway, reduced sea ice, and enhanced cyclonic circulation over the Barents Sea. At the same time, the North Atlantic Oscillation was weakening.

Recent Glacier Retreat Exceeds Internal Variability

Glacier fluctuations exclusively due to internal variations in the climate system are simulated using downscaled integrations of the ECHAM4/OPYC coupled general circulation model (GCM). A process-based modeling approach using a mass balance model of intermediate complexity and a dynamic ice flow model considering simple shearing flow and sliding are applied. Multimillennia records of glacier length fluctuations for Nigardsbreen (Norway) and Rhonegletscher (Switzerland) are simulated using autoregressive processes determined by statistically downscaled GCM experiments. Return periods and probabilities of specific glacier length changes using GCM integrations excluding external forcings such as solar irradiation changes, volcanic, or anthropogenic effects are analyzed and compared to historical glacier length records. Preindustrial fluctuations of the glaciers as far as observed or reconstructed, including their advance during the “Little Ice Age,” can be explained by internal variability in the climate system as represented by a GCM. However, fluctuations comparable to the present-day glacier retreat exceed any variation simulated by the GCM control experiments and must be caused by external forcing, with anthropogenic forcing being a likely candidate.

Connection between Caspian sea level variability and ENSO

The problem of the world greatest lake, the Caspian Sea, level changes attracts the increased attention due to its environmental consequences and unique natural characteristics. Despite the huge number of studies aimed to explain the reasons of the sea level variations the underlying mechanism has not yet been clarified. The important question is to what extent the CSL variability is linked to changes in the global climate system and to what extent it can be explained by internal natural variations in the Caspian regional hydrological system. In this study an evidence of a link between the El Niño/Southern Oscillation phenomenon and changes of the Caspian Sea level is presented. This link was also found to be dominating in numerical experiments with the ECHAM4 atmospheric general circulation model on the 20th century climate.

Intensified Asian summer monsoon and its variability in a coupled model forced by increasing greenhouse gas concentrations

The Asian summer monsoon response to global warming is investigated by a transient green-house warming integration with the ECHAM4/OPYC3 CGCM. It is demonstrated that increases of greenhouse gas concentrations intensify the Asian summer monsoon and its variability. The intensified monsoon results mainly from an enhanced land-sea contrast and a northward shift of the convergence zone. A gradual increase of the monsoon variability is simulated from year 2030 onwards. It seems to be connected with the corresponding increase of the sea surface temperature variability over the tropical Pacific.

The signature of ENSO in the Northern Hemisphere midlatitude seasonal mean flow and high-frequency intraseasonal variability

The impact of pronounced positive and negative sea surface temperature (STT) anomalies in the tropical Pacific associated with the El Niño/Southern Oscillation (ENSO) phenomenon on the atmospheric circulation in the Northern Hemisphere extratropics during the boreal winter season is investigated. This includes both the impact on the seasonal mean flow and on the intraseasonal variability on synoptic time scales. Moreover, the interaction between the transient fluctuations on these times scales and the mean circulation is examined. Both data from an ensemble of five simulations with the ECHAM3 atmospheric general circulation model at a horizontal resolution of T42 each covering the period from 1979 through 1992 and operational analyses from ECMWF for the corresponding period are examined. In each of the simulations observed SSTs for the period of investigation are given as lower boundary forcing, but different atmospheric initial conditions are prescribed. The simulations with ECHAM3 reveal a distinct impact of the pronounced SST-anomalies in the tropical Pacific on the atmospheric circulation in the Northern Hemisphere extratropics during El Niño as well as during La Niña events. These changes in the atmospheric circulation, which are found to be highly significant in the Pacific/North American as well as in the Atlantic/European region, are consistent with the essential results obtained from the analyses. The pronounced SST-anomalies in the tropical Pacific lead to changes in the mean circulation, which are characterized by typical circulation patterns. These changes in the mean circulation are accompanied by marked variations of the activity of the transient fluctuations on synoptic time scales, that are changes in both the kinetic energy on these time scales and the atmospheric transports of momentum and heat accomplished by the short baroclinic waves. The synoptic disturbances, on the other hand, play also an important role in controlling the changes in the mean circulation associated with the ENSO phenomenon. They maintain these typical circulation patterns via barotropic, but counteract them via baroclinic processes. The hypothesis of an impact of the ENSO phenomenon in the Atlantic/European region can be supported. As the determining factor the intensification (reduction) of the Aleutian low and the simultaneous reduction (intensification) of the Icelandic low during El Niño and during La Niña events respectively, is identified. The changes in the intensity of the Aleutian low during the ENSO-events are accompanied by an alteration of the transport of momentum caused by the short baroclinic waves over the North American continent in such a way that the changes in the intensity of the Icelandic low during El Niño as well as during La Niña events are maintained.

Potential Predictability and AMIP Implications of Midlatitude Climate Variability in Two General Circulation Models

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93. The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST. Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.

Stratospheric climate and variability from a general circulation model and observations

The climate and natural variability of the large-scale stratospheric circulation simulated by a newly developed general circulation model are evaluated against available global observations. The simulation consisted of a 30-year annual cycle integration performed with a comprehensive model of the troposphere and stratosphere. The observations consisted of a 15-year dataset from global operational analyses of the troposphere and stratosphere. The model evaluation concentrates on the simulation of the evolution of the extratropical stratospheric circulation in both hemispheres. The December–February climatology of the observed zonal mean winter circulation is found to be reasonably well captured by the model, although in the Northern Hemisphere upper stratosphere the simulated westerly winds are systematically stronger and a cold bias is apparent in the polar stratosphere. This Northern Hemisphere stratospheric cold bias virtually disappears during spring (March–May), consistent with a realistic simulation of the spring weakening of the mean westerly winds in the model. A considerable amount of monthly interannual variability is also found in the simulation in the Northern Hemisphere in late winter and early spring. The simulated interannual variability is predominantly caused by polar warmings of the stratosphere, in agreement with observations. The breakdown of the Northern Hemisphere stratospheric polar vortex appears therefore to occur in a realistic way in the model. However, in early winter the model severely underestimates the interannual variability, especially in the upper troposphere. The Southern Hemisphere winter (June–August) zonal mean temperature is systematically colder in the model, and the simulated winds are somewhat too strong in the upper stratosphere. Contrary to the results for the Northern Hemisphere spring, this model cold bias worsens during the Southern Hemisphere spring (September–November). Significant discrepancies between the model results and the observations are therefore found during the breakdown of the Southern Hemisphere polar vortex. For instance, the simulated Southern Hemisphere stratosphere westerly jet continuously decreases in intensity more or less in situ from June to November, while the observed stratospheric jet moves downward and poleward.