Ulrich Cubasch

SOLAR INFLUENCES ON CLIMATE

The development of this review article has evolved from work carried out by an international team of the International Space Science Institute (ISSI), Bern, Switzerland, and from work carried out under the auspices of Scientific Committee on Solar Terrestrial Physics (SCOSTEP) Climate and Weather of the Sun‐Earth System (CAWSES‐1). The support of ISSI in providing workshop and meeting facilities is acknowledged, especially support from Y. Calisesi and V. Manno. SCOSTEP is acknowledged for kindly providing financial assistance to allow the paper to be published under an open access policy. L.J.G. was supported by the UK Natural Environment Research Council (NERC) through their National Centre for Atmospheric Research (NCAS) Climate program. K.M. was supported by a Marie Curie International Outgoing Fellowship within the 6th European Community Framework Programme. J.L. acknowledges support by the EU/FP7 program Assessing Climate Impacts on the Quantity and Quality of Water (ACQWA, 212250) and from the DFG Project Precipitation in the Past Millennium in Europe (PRIME) within the Priority Program INTERDYNAMIK. L.H. acknowledges support from the U.S. NASA Living With a Star program. G.M. acknowledges support from the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement DE‐FC02‐97ER62402, and the National Science Foundation. We also wish to thank Karin Labitzke and Markus Kunze for supplying an updated Figure 13, Andrew Heaps for technical support, and Paul Dickinson for editorial support. Part of the research was carried out under the SPP CAWSES funded by GFG. J.B. was financially supported by NCCR Climate–Swiss Climate Research.

Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime

Abstract A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with...

Time-dependent greenhouse warming computations with a coupled ocean-atmosphere model

Climate changes during the next 100 years caused by anthropogenic emissions of greenhouse gases have been simulated for the Intergovernmental Panel on Climate Change Scenarios A (“business as usual”) and D (“accelerated policies”) using a coupled ocean-atmosphere general circulation model. In the global average, the near-surface temperature rises by 2.6 K in Scenario A and by 0.6 K in Scenario D. The global patterns of climate change for both IPCC scenarios and for a third step-function 2 x CO2 experiment were found to be very similar. The warming delay over the oceans is larger than found in simulations with atmospheric general circulation models coupled to mixed-layer models, leading to a more pronounced land-sea contrast and a weaker warming (and in some regions even an initial cooling) in the Southern Ocean. During the first forty years, the global warming and sea level rise due to the thermal expansion of the ocean are significantly slower than estimated previously from box-diffusion-upwelling models, but the major part of this delay can be attributed to the previous warming history prior to the start of present coupled ocean-atmosphere model integration (cold start).

An overview of results from the Coupled Model Intercomparison Project

Abstract The Coupled Model Intercomparison Project (CMIP) collects output from global coupled ocean–atmosphere general circulation models (coupled GCMs). Among other uses, such models are employed both to detect anthropogenic effects in the climate record of the past century and to project future climatic changes due to human production of greenhouse gases and aerosols. CMIP has archived output from both constant forcing (“control run”) and perturbed (1% per year increasing atmospheric carbon dioxide) simulations. This report summarizes results form 18 CMIP models. A third of the models refrain from employing ad hoc flux adjustments at the ocean–atmosphere interface. The new generation of non-flux-adjusted control runs are nearly as stable as—and agree with observations nearly as well as—the flux-adjusted models. Both flux-adjusted and non-flux-adjusted models simulate an overall level of natural internal climate variability that is within the bounds set by observations. These developments represent significant progress in the state of the art of climate modeling since the Second (1995) Scientific Assessment Report of the Intergovernmental Panel on Climate Change (IPCC; see Gates et al. [Gates, W.L., et al., 1996. Climate models—Evaluation. Climate Climate 1995: The Science of Climate Change, Houghton, J.T., et al. (Eds.), Cambridge Univ. Press, pp. 229–284]). In the increasing-CO2 runs, differences between different models, while substantial, are not as great as one might expect from earlier assessments that relied on equilibrium climate sensitivity.

Detecting greenhouse-gas-induced climate change with an optimal fingerprint method

Abstract A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of cl...

Towards the detection and attribution of an anthropogenic effect on climate

It has been hypothesized recently that regional-scale cooling caused by anthropogenic sulfate aerosols may be partially obscuring a warming signal associated with changes in greenhouse gas concentrations. Here we use results from model experiments in which sulfate and carbon dioxide have been varied individually and in combination in order to test this hypothesis. We use centered [R(t)] and uncentered [C(t)] pattern similarity statistics to compare observed time-evolving surface temperature change patterns with the model-predicted equilibrium signal patterns. We show that in most cases, the C(t) statistic reduces to a measure of observed global-mean temperature changes, and is of limited use in attributing observed climate changes to a specific causal mechanism. We therefore focus on R(t), which is a more useful statistic for discriminating between forcing mechanisms with different pattern signatures but similar rates of global mean change. Our results indicate that over the last 50 years, the summer (JJA) and fall (SON) observed patterns of near-surface temperature change show increasing similarity to the model-simulated response to combined sulfate aerosol/CO2 forcing. At least some of this increasing spatial congruence occurs in areas where the real world has cooled. To assess the significance of the most recent trends in R(t) and C(t), we use data from multi-century control integrations performed with two different coupled atmosphere-ocean models, which provide information on the statistical behavior of ‘unforced’ trends in the pattern correlation statistics. For the combined sulfate aerosol/CO2 experiment, the 50-year R(t) trends for the JJA and SON signals are highly significant. Results are robust in that they do not depend on the choice of control run used to estimate natural variability noise properties. The R(t) trends for the C02-only signal are not significant in any season. C(t) trends for signals from both the C02-only and combined forcing experiments are highly significant in all seasons and for all trend lengths (except for trends over the last 10 years), indicating large global-mean changes relative to the two natural variability estimates used here. The caveats regarding the signals and natural variability noise which form the basis of this study are numerous. Nevertheless, we have provided first evidence that both the largest-scale (global-mean) and smaller-scale (spatial anomalies about the global mean) components of a combined C02/anthropogenic sulfate aerosol signal are identifiable in the observed near-surface air temperature data. If the coupled-model noise estimates used here are realistic, we can be highly confident that the anthropogenic signal that we have identified is distinctly different from internally generated natural variability noise. The fact that we have been able to detect the detailed spatial signature in response to combined C02 and sulfate aerosol forcing, but not in response to C02 forcing alone, suggests that some of the regional-scale background noise (against which we were trying to detect a C02-only signal) is in fact part of the signal of a sulfate aerosol effect on climate. The large effect of sulfate aerosols found in this study demonstrates the importance of their inclusion in experiments designed to simulate past and future climate change.

Tropical air-sea interaction in general circulation models

An intercomparison is undertaken of the tropical behavior of 17 coupled ocean-atmosphere models in which at least one component may be termed a general circulation model (GCM). The aim is to provide a taxonomy—a description and rough classification—of behavior across the ensemble of models, focusing on interannual variability. The temporal behavior of the sea surface temperature (SST) field along the equator is presented for each model, SST being chosen as the primary variable for intercomparison due to its crucial role in mediating the coupling and because it is a sensitive indicator of climate drift. A wide variety of possible types of behavior are noted among the models. Models with substantial interannual tropical variability may be roughly classified into cases with propagating SST anomalies and cases in which the SST anomalies develop in place. A number of the models also exhibit significant drift with respect to SST climatology. However, there is not a clear relationship between climate drift and the presence or absence of interannual oscillations. In several cases, the mode of climate drift within the tropical Pacific appears to involve coupled feedback mechanisms similar to those responsible for El Niño variability. Implications for coupled-model development and for climate prediction on seasonal to interannual time scales are discussed. Overall, the results indicate considerable sensitivity of the tropical coupled ocean-atmosphere system and suggest that the simulation of the warm-pool/cold-tongue configuration in the equatorial Pacific represents a challenging test for climate model parameterizations.

Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model

Four time-dependent greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different “snapshots” of the control run climate. The radiative forcing — the increase in equivalent CO2 concentrations from 1985–2035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A — was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the “between-experiment” variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial state of the climate system. While the individual members of the ensemble show considerable variation in the pattern and amplitude of near-surface temperature change after 50 years, the ensemble mean climate change pattern closely resembles that obtained in a 100-year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. While the ensemble mean surface temperature and sea level fields show regionally significant responses to greenhouse-gas forcing, it is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations.

A model‐data comparison of European temperatures in the Eemian interglacial

Compared to the wide range of different models used for the analysis of the climate system, GCMs have the most complex representation of the atmospheric physical processes. We have chosen the ECHO-G model as a state-ofthe-art OA-GCM to simulate the climatic conditions at 125 kyr BP by adapting the orbital parameters and greenhouse gas concentrations. [4] For terrestrial palaeoclimate reconstructions, the use of botanical fossils is well-established, because vegetation is in close relation to climate. Mainly pollen can be found in large numbers in suitable sediments, and macro remains add valuable information for important climate indicator species. For the reconstructions, recently developed botanical � � � � � � � � � � � � � � � � � � � � � ��

Climate change projections and stratosphere-troposphere interaction

Climate change is expected to increase winter rainfall and flooding in many extratropical regions as evaporation and precipitation rates increase, storms become more intense and storm tracks move polewards. Here, we show how changes in stratospheric circulation could play a significant role in future climate change in the extratropics through an additional shift in the tropospheric circulation. This shift in the circulation alters climate change in regional winter rainfall by an amount large enough to significantly alter regional climate change projections. The changes are consistent with changes in stratospheric winds inducing a change in the baroclinic eddy growth rate across the depth of the troposphere. A change in mean wind structure and an equatorward shift of the tropospheric storm tracks relative to models with poor stratospheric resolution allows coupling with surface climate. Using the Atlantic storm track as an example, we show how this can double the predicted increase in extreme winter rainfall over Western and Central Europe compared to other current climate projections.