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The Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) is a protocol-driven international initiative under the umbrella of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) aiming at coordinating the activities of different Research Institutes involved in numerical climate modelling focused on a multi-model assessment of climate models' performance under strong volcanic forcing conditions. The main objective of the initiative is to assess to what extent responses of the coupled ocean-atmosphere system to the same applied strong volcanic forcing are robustly simulated across state-of-the-art coupled climate models and identify the causes that limit robust simulated behavior, especially differences in their treatment of physical processes. To this purpose, four Tier-1 (mandatory) experiments branched into two main sets, named “volc-pinatubo” and “volc-long” were defined, together with eight more lower-priority experiments. Six years since the definition of the VolMIP protocol (Zanchettin et al., 2016), ensemble simulations of most of the mandatory VolMIP experiments have been completed and made publicly available through the Earth System Grid Federation open platform, with the first VolMIP results being currently published and several analyses in progress. The long turnover time between the experiment design, the integration of the simulations and the analysis of the output motivates an assessment of the overall effectiveness of the VolMIP strategy, particularly in the light of a possible second phase of the initiative.In this contribution, we will illustrate the status of the initiative, highlight its major achievements and discuss its future perspective in the light of emergent scientific questions regarding volcanically forced climate variability.Zanchettin, D., Khodri, M., Timmreck, C., Toohey, M., Schmidt, A., Gerber, E. P., Hegerl, G., Robock, A., Pausata, F. S. R., Ball, W. T., Bauer, S. E., Bekki, S., Dhomse, S. S., LeGrande, A. N., Mann, G. W., Marshall, L., Mills, M., Marchand, M., Niemeier, U., Poulain, V., Rozanov, E., Rubino, A., Stenke, A., Tsigaridis, K., and Tummon, F.: The Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP): experimental design and forcing input data for CMIP6, Geosci. Model Dev., 9, 2701–2719, https://doi.org/10.5194/gmd-9-2701-2016, 2

The European Climate Prediction system (EUCP) project aimed to lay the foundation for a future regional climate prediction system for Europe. An important element of this is the role of narrative or storylines approaches in data production and scientific investigation, as well as a user product. We will present the results of our investigations which sought to understand the potential advantages and challenges in developing physically based climate storylines as part of a climate service by addressing the questionsWhat are climate storylines and where are they useful? How could storylines bring together various outputs of EUCP state-of-the-art climate science attempting to reduce uncertainty and complexity in climate projections, and seamlessly combine them with decadal predictions? What are the challenges of producing them as a service? The body of EUCP work included two case studies of co-producing storylines as a user product, revealing the potential usefulness for applications. These included the heritage and water supply management sectors which are at different stages of adaptation management. We also reflect on the potential of event-based future storylines: one using a convection-permitting model to provide attribution statements for the Copenhagen flooding event in July 2011 and another using large ensembles to construct storylines of the European summer 2018 drought under different pseudo-global warming. Novel scientific studies were also performed which could form the scientific building blocks of climate storylines including an algorithmic clustering approach and approaches to producing more realistic climate variability. Finally, we will present a tool for performing multiple lines of evidence assessments that could aid storylines development.

Skillful, reliable and seamless climate information for the next 1-40 years is crucial for policy- and other decision makers to develop suitable planning strategies. This poses a challenge for the scientific community, which is split up into the prediction community (developing initialized predictions up to multi-annual time scales, e.g. 10 years), and the climate projection community (providing long-term projections). As predictions are initialized with the observed climate state at the start of the integration, they are often more skillful for lead times of a few years (depending on variable and region) compared to uninitialized climate projections, which can provide information beyond 10 years. Thus, most useful climate information for the next 1-40 years would likely need to draw upon information from both sources. However, temporal merging from different sources is challenging, e.g., it can lead to discontinuities in the central estimates at the respective transition points, which pose problems for interpretation and communication alike.The aim of this study is to explore if skillful and seamless climate information can be provided by applying a model weighting scheme to initialized decadal predictions and projections. The model specific weights are based on the respective past model performance compared to observations. Whereas for climate projections each model is assigned a single weight, for initialized decadal predictions these weights are calculated for each forecast year separately. Here, we apply the weighting technique to CMIP6 decadal predictions and climate projections from 8 different models.

The central United States experienced the hottest summers of the twentieth century in 1934 and 1936, with over 40 heat wave days and maximum temperatures surpassing 44°C at some locations like Kansas and Oklahoma. In fact, as of 2019, the summer of 1936 is still the hottest on record. The heat waves coincided with the decade-long Dust Bowl drought, that caused wide-spread crop failures, dust storms that penetrated to New York and considerable out-migration. In a very-large ensemble regional modelling framework, we show that greenhouse gas increases slightly enhanced the frequency and duration of the Dust Bowl heat waves, and would strongly enhance similar heat waves in the present day under current, further elevated greenhouse gas levels. Specifically, present-day atmospheric greenhouse gas forcing would reduce the return period of a rare (less than once in a century) heat wave summer as observed in 1936 to about 1-in 40-years, with further contribution by sea surface warming. Here, we show that a key driver of this elevated heat wave risk is the reduction in evaporative cooling and increase in sensible heating during dry springs and summers. Hence, we conclude that a warmer world is creating the potential for future extreme heat in moisture-limited regions, with potentially very damaging impacts.

The wet-gets-wetter, dry-gets-drier paradigm (WWDD) is widely used to summarize the expected response of the hydrological cycle to global warming. While some studies find that changes in observations and climate models support the WWDD paradigm, others find that it is more complicated at local scales and over land. This discrepancy is partly explained by differences in model climatologies and by movement of the wet and dry regions. Here we show that by tracking changes in wet and dry regions as they shift over the tropics and vary in models, mean precipitation changes follow the WWDD pattern in observations and models over land and ocean. However, this signal is reduced and disappears in model dry regions, when these factors are not accounted for. Accounting for seasonal and interannual shifts of the regions and climatological differences between models reduces uncertainty in predictions of future precipitation changes and makes these changes detectable earlier.

Summer 2012 was very wet in northern Europe, and unusually dry and hot in southern Europe. We use multiple approaches to determine whether anthropogenic forcing made the extreme European summer of 2012 more likely. Using a number of observation- and model-based methods, we find that there was an anthropogenic contribution to the extremes in southern Europe, with a qualitative consensus across all methodologies. There was a consensus across the methodologies that there has been a significant increase in the risk of hot summers in southern Europe with climate change. Most approaches also suggested a slight drying, but none of the results were statistically significant. The unusually wet summer in northern Europe was made more likely by the observed atmospheric circulation pattern in 2012, but no evidence was found for a long-term trend in circulation. ISSN:0930-7575 ISSN:1432-0894

Water isotope-enabled coupled atmosphere–ocean climate models allow for exploration of the relative contributions to coral stable oxygen isotope (δ18Ocoral) variability arising from sea surface temperature (SST) and the isotopic composition of seawater (δ18Osw). The unforced behaviour of the isotope-enabled HadCM3 coupled general circulation model suggests that the extent to which inter-annual δ18Osw variability contributes to that in model δ18Ocoral is strongly spatially dependent, ranging from being negligible in the eastern equatorial Pacific to accounting for 50% of δ18Ocoral variance in parts of the western Pacific. In these latter cases, a significant component of the inter-annual δ18Osw variability is correlated to that in SST, meaning that local calibrations of the effective local δ18Ocoral–SST relationships are likely to be essential. Furthermore, the relationship between δ18Osw and SST can be non-linear, such that the model interpretation of central and western equatorial Pacific δ18Ocoral in the context of a linear dependence on SST alone leads to overestimation (by up to 20%) of the SST anomalies associated with large El Niño events. Intra-model evaluation of a salinity-based pseudo-coral approach shows that such an approach captures the first-order features of the model δ18Osw behaviour. However, the utility of the pseudo-corals is limited by the extent of spatial variability seen within the modelled slopes of the temporal salinity–δ18Osw relationship.

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала. Large-scale millennial length Northern Hemisphere (NH) temperature reconstructions have been progressively improved over the last 20 years as new datasets have been developed. This paper, and its companion (Part II, Anchukaitis et al. in prep), details the latest tree-ring (TR) based NH land air temperature reconstruction from a temporal and spatial perspective. This work is the first product of a consortium called N-TREND (Northern Hemisphere Tree-Ring Network Development) which brings together dendroclimatologists to identify a collective strategy for improving large-scale summer temperature reconstructions. The new reconstruction, N-TREND2015, utilises 54 records, a significant expansion compared with previous TR studies, and yields an improved reconstruction with stronger statistical calibration metrics. N-TREND2015 is relatively insensitive to the compositing method and spatial weighting used and validation metrics indicate that the new record portrays reasonable coherence with large scale summer temperatures and is robust at all time-scales from 918 to 2004 where at least 3 TR records exist from each major continental mass. N-TREND2015 indicates a longer and warmer medieval period (~900 e1170) than portrayed by previous TR NH reconstructions and by the CMIP5 model ensemble, but with better overall agreement between records for the last 600 years. Future dendroclimatic projects should focus on developing new long records from data-sparse regions such as North America and eastern Eurasia as well as ensuring the measurement of parameters related to latewood density to complement ring-width records which can improve local based calibration substantially.

Here, the Climate Symposium 2014, organized by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Climate Research Programme (WCRP), with support from the European Commission (EC), European Space Agency (ESA), and other agencies, took place in Darmstadt, Germany, from 13 to 17 October 2014. Around 500 participants from 49 countries attended the event and represented over 200 organizations. Another 500 individuals participated remotely via “live streaming.”

Arising from C. Mora et al. , 183–187 (2013) The question of when the signal of climate change will emerge from the background noise of climate variability—the ‘time of emergence’—is potentially important for adaptation planning. Mora et al.1 presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions2. There is a Reply to this Brief Communication Arising by Mora, C. et al. Nature 511, http://dx.doi.org/10.1038/nature13524 (2014).

Fingerprint techniques for the detection of anthropogenic climate change aim to distinguish the climate response to anthropogenic forcing from responses to other external influences and from internal climate variability. All these responses and the characteristics of internal variability are typically estimated from climate model data. We evaluate the sensitivity of detection and attribution results to the use of response and variability estimates from two different coupled ocean atmosphere general circulation models (HadCM2, developed at the Hadley Centre, and ECHAM3/LSG from the MPI fur Meteorologie and Deutsches Klimarechenzentrum). The models differ in their response to greenhouse gas and direct sulfate aerosol forcing and also in the structure of their internal variability. This leads to differences in the estimated amplitude and the significance level of anthropogenic signals in observed 50-year summer (June, July, August) surface temperature trends. While the detection of anthropogenic influence on climate is robust to intermodel differences, our ability to discriminate between the greenhouse gas and the sulfate aerosol signals is not. An analysis of the recent warming, and the warming that occurred in the first half of the twentieth century, suggests that simulations forced with combined changes in natural (solar and volcanic) and anthropogenic (greenhouse gas and sulfate aerosol) forcings agree best with the observations.

[1] Climate models predict substantial changes in seasonal precipitation in the future. Anthropogenic forcing has been found to contribute to the observed pattern of land precipitation change over the 2nd half of the 20th century when annual precipitation is averaged within latitude bands, the observed change was substantially larger than response simulated in climate models, based on a single observational dataset. Here we investigate the robustness of this finding using several land only observational datasets and look for an explanation for why observed changes are significantly larger. We show the discrepancy between model simulated and observed trends is reduced when changes are expressed as percent climatology, which reduces the difference in scale between observed point locations and model gridboxes. Focusing on seasonal rather than annual data reveals that there are seasonal differences in the pattern of zonal precipitation changes over the 20th century. We use fingerprint for zonal precipitation changes from 54 CMIP3 simulations and show that observed changes are detectable in all seasons but boreal summer (JJA), even when doubling the variance of the model simulation, and irrespective of the dataset used. The observed change is still larger than that simulated by the multi-model mean in all datasets except in boreal summer but only in boreal spring is the observed change robustly and significantly larger than that simulated.

[1] In this study we analyse gridded observed and multi‐ model simulated trends in the annual number of warm nights during the second half of the 20th century. We show that there is evidence that external forcing has significantly increased the number of warm nights, both globally and over many regions. We define thirteen regions with a high density of observational data over two datasets, for which we compare observed and simulated trends from 20th century simulations. The main analysis period is 1951–1999, with a sub‐period of 1970–1999. In order to investigate if observed trends changed past 1999, we also analysed periods of 1955–2003 and 1974–2003. Both observed and ensemble mean model data from all models analysed show a positive trend for the regional mean number of warm nights in all regions within this 49 year period (1951–1999). The trends tend to become more pronounced over the sub‐period 1970–1999 and even more so up to 2003. We apply a fingerprint analysis to assess if trends are detectable relative to internal climate variability.Wefindthat changes in the globalscale analysis, and in 9 out of 13 regions, are detectable at the 5% significance level. A large part of the observed global‐scale trend in TN90 results from the trend in mean temperature, which has been attributed largely to anthropogenic greenhouse gas increase. This suggests that the detected global‐ scale trends in the number of warm nights are at least partly anthropogenic. Citation: Morak,S., G.C. Hegerl,andJ. Kenyon (2011), Detectable regional changes in the number of warm nights, Geophys. Res. Lett., 38, L17703, doi:10.1029/2011GL048531.

In its Fifth Assessment Report, the Intergovernmental Panel on Climate Change concluded that it is “extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together” ( 1 ). This conclusion was based on an expert assessment drawing on multiple analyses of observed temperature changes. However, substantial uncertainties remain, especially in estimating the human contribution to regional temperature change and extreme events.

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Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this "cold start" error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C02 concentrations stipulated in Scenario A ("Business as Usual") of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990-2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015-2025 and 2005-2015, respectively. The reduction of the "cold start" error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust. © 1995 Springer-Verlag.

Three 30 year long simulations have been performed with a T42 atmosphere model, in which the sea-surface temperature (SST) and sea-ice distribution have been taken from a transient climate change experiment with a T21 global coupled ocean-atmosphere model. In this so-called time-slice experiment, the SST values (and the greenhouse gas concentration) were taken at present time CO2 level, at the time of CO2 doubling and tripling. The annual cycle of temperature and precipitation has been studied over the IPCC regions and has been compared with observations. Additionally the combination of temperature and precipitation change has been analysed. Further parameters investigated include the difference between daily minimum and maximum temperature, the rainfall intensity and the length of droughts. While the regional simulation of the annual cycle of the near surface temperature is quite realistic with deviations rarely exceeding 3 K, the precipitation is reproduced to a much smaller degree of accuracy. The changes in temperature at the time of CO2 doubling amount to only 30-40 of those at the 3 * CO2 level and show hardly any seasonal variation, contrary to the 3 * CO2 experiment. The comparatively small response to the CO2 doubling can be attributed to the cold-start of the simulation, from which the SST has been extracted. The strong change in the seasonality cannot be explained by internal fluctuations and cold start alone, but has to be caused by feedback mechanisms. Due to the delay in warming caused by the transient experiment, from which the SST has been derived, the 3 * CO2 experiment can be compared to the CO2 doubling studies performed with mixed-layer models. The precipitation change does not display a clear signal. However, an increase of the rain intensity and of longer dry periods is simulated in many regions of the globe. The changes in these parameters as well as the combination of temperature- and precipitation change and the changes in the daily temperature range give valuable hints, in which regions observational studies should be intensified and under which aspects the observational data should be evaluated. © 1995 Kluwer Academic Publishers.

[2] The IPCC Third Assessment Report [Mitchell et al., 2001] describes the application of ‘‘optimal fingerprinting’’ techniques [Hasselmann, 1997] to the detection of a combined greenhouse gas plus sulphate aerosol (GS) response over the past 50 years. The response patterns simulated by seven climate models were found to be detectable in observations of surface temperature; the amplitudes of these patterns in the observations were found to be inconsistent with zero. However, there were considerable differences in the magnitude of the response between the models, with some simulating a response consistent with that observed, while others predicted a response significantly larger than that observed. When these techniques were used to estimate the amplitudes of the greenhouse gas (G) and sulphate aerosol (S) response patterns separately, these inter-model differences became larger, with simultaneous detection of G and S possible with some models, but not with others. These results from multiple models are synthesized only qualitatively by Mitchell et al. [2001]. Here we suggest a method for doing so more quantitatively. [3] Lambert and Boer [2001] compared coupled model climatologies of surface air temperature with observations using the CMIP ensemble. They found that overall the mean climatology of the models matched the observations better than that of any individual model. Similarly in seasonal forecasting, Krishnamurti et al. [1999] and Kharin and Zwiers [2002] argued that a weighted sum of multiple model predictions performs better than predictions using an individual model. These results might be explained if each model has independent errors, each giving different characteristic biases in model output. Thus, much as we can reduce the effects of initial condition uncertainty by averaging over an ensemble of integrations with perturbed initial conditions, so we might also account for model uncertainty by averaging over multiple models. Such an argument is also likely to apply to the anthropogenically-forced responses of these models. Thus here we describe how a mean of the response patterns of five climate models (HadCM2, HadCM3, ECHAM3, CGCM1 and CGCM2) may be used to detect greenhouse gas and sulphate aerosol influences in surface air temperature. [4] In one standard approach to the detection of anthropogenic influence on surface temperature [Allen et al., 2002], signal-to-noise optimised observations are regressed against a modelled response pattern, using a total least squares fit. Climate model output enters the analysis at three points. First, output from integrations of the model with prescribed time-varying forcings is used to derive the signal patterns of climate response. Second, output from a control integration is used to estimate the autocovariance matrix representing internal variability. This covariance matrix is used to derive the EOF basis used for truncation of the signal patterns and signal-to-noise optimisation. Third, an independent section of control data is used to estimate the uncertainty in the derived regression coefficients. In this study, output from multiple models is used in all three stages.