G. J. van Oldenborgh

Reliability of regional climate model trends

A necessary condition for a good probabilistic forecast is that the forecast system is shown to be reliable: forecast probabilities should equal observed probabilities verified over a large number of cases. As climate change trends are now emerging from the natural variability, we can apply this concept to climate predictions and compute the reliability of simulated local and regional temperature and precipitation trends (1950–2011) in a recent multi-model ensemble of climate model simulations prepared for the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5). With only a single verification time, the verification is over the spatial dimension. The local temperature trends appear to be reliable. However, when the global mean climate response is factored out, the ensemble is overconfident: the observed trend is outside the range of modelled trends in many more regions than would be expected by the model estimate of natural variability and model spread. Precipitation trends are overconfident for all trend definitions. This implies that for near-term local climate forecasts the CMIP5 ensemble cannot simply be used as a reliable probabilistic forecast.

How unusual was autumn 2006 in Europe

Abstract. The temperatures in large parts of Europe have been record high during the meteorological autumn of 2006. Compared to 1961–1990, the 2 m temperature was more than three degrees Celsius above normal from the North side of the Alps to southern Norway. This made it by far the warmest autumn on record in the United Kingdom, Belgium, the Netherlands, Denmark, Germany and Switzerland, with the records in Central England going back to 1659, in the Netherlands to 1706 and in Denmark to 1768. The deviations were so large that under the obviously false assumption that the climate does not change, the observed temperatures for 2006 would occur with a probability of less than once every 10 000 years in a large part of Europe, given the distribution defined by the temperatures in the autumn 1901–2005. A better description of the temperature distribution is to assume that the mean changes proportional to the global mean temperature, but the shape of the distribution remains the same. This includes to first order the effects of global warming. Even under this assumption the autumn temperatures were very unusual, with estimates of the return time of 200 to 2000 years in this region. The lower bound of the 95% confidence interval is more than 100 to 300 years. Apart from global warming, linear effects of a southerly circulation are found to give the largest contributions, explaining about half of the anomalies. SST anomalies in the North Sea were also important along the coast. Climate models that simulate the current atmospheric circulation well underestimate the observed mean rise in autumn temperatures. They do not simulate a change in the shape of the distribution that would increase the probability of warm events under global warming. This implies that the warm autumn 2006 either was a very rare coincidence, or the local temperature rise is much stronger than modelled, or non-linear physics that is missing from these models increases the probability of warm extremes.

Toward an Integrated Seasonal Forecasting System for South America

Abstract This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: (i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and (ii) a multimodel system composed of three European coupled ocean–atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and cali...

A Bayesian approach for multi‐model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

a river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Evaluation of trends in high temperature extremes in north-western Europe in regional climate models

Projections of future changes in weather extremes on the regional and local scale depend on a realistic representation of trends in extremes in regional climate models (RCMs). We have tested this assumption for moderate high temperature extremes (the annual maximum of the daily maximum 2 m temperature, Tann:max). Linear trends in Tann:max from historical runs of 14 RCMs driven by atmospheric reanalysis data are compared with trends in gridded station data. The ensemble of RCMs significantly underestimates the observed trends over most of the north-western European land surface. Individual models do not fare much better, with even the best performing models underestimating observed trends over large areas. We argue that the inability of RCMs to reproduce observed trends is probably not due to errors in large-scale circulation. There is also no significant correlation between the RCM Tann:max trends and trends in radiation or Bowen ratio. We conclude that care should be taken when using RCM data for adaptation decisions.

Comparison of methods: Attributing the 2014 record European temperatures to human influences

The year 2014 broke the record for the warmest yearly average temperature in Europe. Attributing how much this was due to anthropogenic climate change and how much it was due to natural variability is a challenging question but one that is important to address. In this study, we compare four event attribution methods. We look at the risk ratio (RR) associated with anthropogenic climate change for this event, over the whole European region, as well as its spatial distribution. Each method shows a very strong anthropogenic influence on the event over Europe. However, the magnitude of the RR strongly depends on the definition of the event and the method used. Across Europe, attribution over larger regions tended to give greater RR values. This highlights a major source of sensitivity in attribution statements and the need to define the event to analyze on a case-by-case basis.

Preparing local climate change scenarios for the Netherlands using resampling of climate model output

A method to prepare a set of four climate scenarios for the Netherlands is presented. These scenarios for climate change in 2050 and 2085 (compared to present-day) are intended for general use in climate change adaptation in the Netherlands. An ensemble of eight simulations with the global model EC-Earth and the regional climate model RACMO2 (run at 12 km resolution) is used. For each scenario time horizon, two target values of the global mean temperature rise are chosen based on the spread in the CMIP5 simulations. Next, the corresponding time periods in the EC-Earth/RACMO2 simulations are selected in which these target values of the global temperature rise are reached. The model output for these periods is then resampled using blocks of 5 yr periods. The rationale of resampling is that natural variations in the EC-Earth/RACMO2 ensemble are used to represent (part of the) uncertainty in the CMIP5 projections. Samples are then chosen with the aim of reconstructing the spread in seasonal temperature and precipitation changes in CMIP5 for the Netherlands. These selected samples form the basis of the scenarios. The resulting four scenarios represent 50–80% of the CMIP5 spread for summer and winter changes in seasonal means as well as a limited number of monthly statistics (warm, cold, wet and dry months). The strong point of the method—also in relation to the previous set of the climate scenarios for the Netherlands issued in 2006—is that it preserves nearly all physical inter-variable consistencies as they exist in the original model output in both space and time.

Claim of solar influence is on thin ice: are 11-year cycle solar minima associated with severe winters in Europe?

A recent paper in Geophysical Research Letters, ‘Solar influence on winter severity in central Europe’, by Sirocko et al (2012 Geophys. Res. Lett. 39 L16704) claims that ‘weak solar activity is empirically related to extremely cold winter conditions in Europe’ based on analyses of documentary evidence of freezing of the River Rhine in Germany and of the Reanalysis of the Twentieth Century (20C). However, our attempt to reproduce these findings failed. The documentary data appear to be selected subjectively and agree neither with instrumental observations nor with two other reconstructions based on documentary data. None of these datasets show significant connection between solar activity and winter severity in Europe beyond a common trend. The analysis of Sirocko et al of the 20C circulation and temperature is inconsistent with their time series analysis. A physically-motivated consistent methodology again fails to support the reported conclusions. We conclude that multiple lines of evidence contradict the findings of Sirocko et al.

The Role of Anthropogenic Warming in 2015 Central European Heat Waves

Summer 2015 in Europe. The summer 2015 in Europe was highly unusual, as persistent heat and dryness prevailed in large parts of the continent. In central and eastern Europe, a combination of record-low seasonal rainfall (Orth et al. 2016) and record-high monthly July/August temperatures were observed over an area stretching from France to western Russia (Supplemental Fig. S11.1). The anomalous temperatures were caused by a sequence of four intense heat waves that struck the region from the end of June to early September (e.g., Fig. 11.1a). It is precisely the few-day heat that causes problems with human health, especially when combined with high humidity (McGregor et al. 2010). We analyze seasonal maxima of 3-day mean temperature (Tair3d, max) and seasonal maxima of 3-day daily maximum wet bulb temperature (WBTX3d, max), a measure of human thermal discomfort that combines temperature and humidity and is a proxy for heat stress on the human body (Fischer and Knutti 2013; Sherwood and Huber 2010). The series of heat waves began with a strongly meandering jet stream, that is summertime “omegablocking” (Dole et al. 2011), and the advection of very warm subtropical air into central and western Europe (Supplemental Fig. S11.1). Later in the season, the jet stream was displaced to the north, so that stable high-pressure systems could prevail over central and eastern Europe bringing heat there. The first heat wave in early July was hence most pronounced in western parts of the continent, while south-central and east-central Europe experienced the highest temperatures in the subsequent heat waves later in the season (Fig. 11.1b). Anomalies in the hottest 3-day mean temperature reached up to +6°C relative to climatology (Figs. 11.1c,d), and temperature records were broken, including nationwide records (Kitzingen, Germany: 40.3°C; https://weather.com/news/climate/news/europe-heat-wave-poland-germany-czech-august-2015), various station records stretching from France to the Balkan countries and southern Sweden (www .meteofrance.fr/actua l ites/26913226-episode -de-tres-fortes-chaleurs-en-france), nighttime temperatures (Vienna, Austria: 26.9°C), record 3-day mean temperatures across central Europe (Fig. 11.1e), and inland water temperatures (e.g., Lake Constance). Europe experienced the hottest August ever recorded (NOAA 2016), and the entire summer season ranked third after the unusual summers of persistent heat in 2003 and 2010 with hotspots in France and western Russia, respectively (Barriopedro et al. 2011; Stott et al. 2004). This extraordinary sequence of events raises the question to what extent human-induced climate change played a role in short-term heat waves beyond natural climate variability. A potential anthropogenic contribution to the summer 2015 heat events had already been investigated in near–real time (www.climatecentral.org /europe-2015-heatwave-climate-change), and in the present paper we build upon and substantiate the previous analysis. We investigate two diagnostics (Tair3d, max and WBTX3d, max) at four locations in longterm station-based observational records and in a large ensemble of consistently bias-corrected regional climate model simulations.

Trends in high-precipitation events in Jakarta made the flooding of January 2014 more likely

The January 2014 floods paralyzed nearly all of Jakarta, Indonesia. The precipitation events that lead to these floods were not very unusual but show positive trends in the observed record.