Andreas Hense

The Regional Climate Model COSMO-CLM (CCLM)

Meteorologisches Institut Universitat Bonn, Germany¨In the early 1990s the German Weather Service (DWD) found that future demands on weather forecasting wouldrequire convection resolving weather simulation. This required grid mesh sizes much less than ten kilometres,which could not be achieved by the Deutschlandmodell (DM), the operational model at that time. DM wasa hydrostatic model and thus limited by physical reasons to grid mesh sizes larger than about ten kilometres.Therefore the DWD decided to develop a new non-hydrostatic model, the Lokalmodell (LM). The LM supersededthe DM as operational weather forecast model in 1999 and after several improvements met the expectations inseveral respects.The same arguments seem to be valid for climate simulations. Furthermore, most regional climate models(RCMs)originateinaweatherforecastmodel.ThereforeitwasnotsurprisingthatscientistsatthePotsdamInstituteforClimateImpactResearch(PIK)tookintoaccounttheLMasanoptionwhentheylookedforanappropriateRCMfor their WAVES project (B

The Convective and Orographically Induced Precipitation Study:A Research and Development Project of the World Weather Research Program for Improving Quantitative Precipitation Forecasting in Low-mountain Regions

Abstract The international field campaign called the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007 in southwestern Germany/eastern France. The overarching goal of COPS is to advance the quality of forecasts of orographically-induced convective precipitation by four-dimensional observations and modeling of its life cycle. COPS was endorsed as one of the Research and Development Projects of the World Weather Research Program (WWRP), and combines the efforts of institutions and scientists from eight countries. A strong collaboration between instrument principal investigators and experts on mesoscale modeling has been established within COPS. In order to study the relative importance of large-scale and small-scale forcing leading to convection initiation in low mountains, COPS is coordinated with a one-year General Observations Period in central Europe, the WWRP Forecast Demonstration Project MAP D-PHASE, and the first summertime European THORPEX Regional...

Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

Abstract A statistical downscaling approach for extremes using censored quantile regression is presented. Conditional quantiles of station data (e.g., daily precipitation sums) in Germany are estimated by means of the large-scale circulation as represented by the NCEP reanalysis data. It is shown that a mixed discrete–continuous response variable, such as a daily precipitation sum, can be statistically modeled by a censored variable. Furthermore, a conditional quantile skill score is formulated to assess the relative gain of a quantile forecast compared with a reference forecast. Just like multiple regression for expectation values, quantile regression provides a tool to formulate a model output statistics system for extremal quantiles.

Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G — I. Near-surface temperature, precipitation and mean sea level pressure

The internal variability in a 1000-yr control simulation with the coupled atmosphere’ocean global climate model ECHO-G is analysed using near-surface temperature, precipitation and mean sea level pressure variables, and is compared with observations and other coupled climate model simulations. ECHO-G requires annual mean flux adjustments for heat and freshwater in order to simulate no significant climate drift for 1000 yr, but no flux adjustments for momentum. The ECHO-G control run captures well most aspects of the observed seasonal and annual climatology and of the interannual to decadal variability of the three variables. Model biases are very close to those in ECHAM4 (atmospheric component of ECHO-G) stand-alone integrations with prescribed observed sea surface temperature. A trend comparison between observed and modelled near-surface temperatures shows that the observed near-surface globalwarming is larger than internal variability produced by ECHO-G, supporting previous studies. The simulated global mean near-surface temperatures, however, show a 2-yr spectral peak which is linked with a strong biennial bias of energy in the El Niño Southern Oscillation signal. Consequently, the interannual variability (3–9 yr) is underestimated.

Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G – II. El Niño Southern Oscillation and North Atlantic Oscillation

A 1000-yr control simulation (CTL) performed with the atmosphere’ocean global climate model ECHO-G is analysed with regard to the El Niño Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), the two major natural climatic variabilities, in comparison with observations and other model simulations. The ENSO-related sea surface temperature climate and its seasonal cycle in the tropical Pacific and a single Intertropical Convergence Zone in the eastern tropical Pacific are simulated reasonably, and the ENSO phase-locking to the annual cycle and the subsurface ocean behaviour related to equatorial wave dynamics are also reproduced well. The simulated amplitude of the ENSO signal is however too large and its occurrence is too regular and frequent. Also, the observed westward propagation of zonal wind stress over the equatorial Pacific is not captured by the model. Nevertheless, the ENSO-related teleconnection patterns of near-surface temperature (T2m), precipitation (PCP) and mean sea level pressure (MSLP) are reproduced realistically. The NAO index, defined as the MSLP difference between Gibraltar and Iceland, has a ‘white’ noise spectrum similar to that of the detrended index obtained from observed data. The correlation and regression patterns of T2m, PCP and MSLP with the NAO index are also successfully simulated. However, the model overestimates the warming over the North Pacific in the high index phase of the NAO, a feature it shares with other coupled models. This might be associated with an enhanced Atlantic’Pacific teleconnection, which is hardly seen in the observations. A detection analysis of the NAO index shows that the observed recent 40–60 yr trend cannot be explained by the model’s internal variability while the recent 20–30 yr trend occurs with a more than 1% chance in ECHO-G CTL.

Probabilistic climate change predictions applying Bayesian model averaging

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation–maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070–2099) SATs while there is only a little effect of Bayesian weighting on the 5–95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

A Bayesian Assessment of Climate Change Using Multimodel Ensembles. Part I: Global Mean Surface Temperature

A Bayesian approach is applied to the observed global surface air temperature (SAT) changes using multimodel ensembles (MMEs) of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations and single-model ensembles (SMEs) with the ECHO-G coupled climate model. A Bayesian decision method is used as a tool for classifying observations into given scenarios (or hypotheses). The prior probability of the scenarios, which represents a degree of subjective belief in the scenarios, is changed into the posterior probability through the likelihood where observations enter, and the posterior is used as a decision function. In the identical prior case the Bayes factor (or likelihood ratio) becomes a decision function and provides observational evidence for each scenario against a predefined reference scenario. Four scenarios are used to explain observed SAT changes: “CTL” (control or no change), “Nat” (natural forcing induced change), “GHG” (greenhouse gas–induced change), and “All” (natural plus anthropogenic forcing–induced change). Observed and simulated global mean SATs are decomposed into temporal components of overall mean, linear trend, and decadal variabilities through Legendre series expansions, coefficients of which are used as detection variables. Parameters (means and covariance matrices) needed to define the four scenarios are estimated from SMEs or MMEs. Taking the CTL scenario as reference one, application results for global mean SAT changes for the whole twentieth century (1900–99) show “decisive” evidence (logarithm of Bayes factor 5) for the All scenario only. While “strong” evidence (log of Bayes factor 2.5) for both the Nat and All scenarios are found in SAT changes for the first half (1900–49), there is decisive evidence for the All scenario for SAT changes in the second half (1950–99), supporting previous results. It is demonstrated that the Bayesian decision results for global mean SATs are largely insensitive to both intermodel uncertainties and prior probabilities.

The convective and orographically-induced precipitation study

Abstract The international field campaign called the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007 in southwestern Germany/eastern France. The overarching goal of COPS is to advance the quality of forecasts of orographically-induced convective precipitation by four-dimensional observations and modeling of its life cycle. COPS was endorsed as one of the Research and Development Projects of the World Weather Research Program (WWRP), and combines the efforts of institutions and scientists from eight countries. A strong collaboration between instrument principal investigators and experts on mesoscale modeling has been established within COPS. In order to study the relative importance of large-scale and small-scale forcing leading to convection initiation in low mountains, COPS is coordinated with a one-year General Observations Period in central Europe, the WWRP Forecast Demonstration Project MAP D-PHASE, and the first summertime European THORPEX Regional...

Mean versus extreme climate in the Mediterranean region and its sensitivity to future global warming conditions

The Mediterranean region (MTR) has been supposed to be very sensitive to changes in land surface and atmospheric greenhouse-gas (GHG) concentrations. Particularly, an intensification of climate extremes may be associated with severe socio-economic implications. Here, we present an analysis of climate mean and extreme conditions in this subtropical area based on regional climate model experiments, simulating the present-day and possible future climate. The analysis of extreme values (EVs) is based on the assumption that the extremes of daily precipitation and near-surface temperature are well fitted by the Generalized Pareto distribution (GPD). Return values of extreme daily events are determined using the method of L-moments. Particular emphasis is laid on the evaluation of the return values with respect to the uncertainty range of the estimate as derived from a Monte Carlo sampling approach. During the most recent 25 years the MTR has become dryer in spring but more humid especially in the western part in autumn and winter. At the same time, the whole region has been subject to a substantial warming. The strongest rainfall extremes are simulated in autumn over the Mediterranean Sea around Italy. Temperature extremes are most pronounced over the land masses, especially over northern Africa. Given the large uncertainty of the EV estimate, only 1-year return values are further analysed. During recent decades, statistically significant changes in extremes are only found for temperature. Future climate conditions may come along with a decrease in mean and extreme precipitation during the cold season, whereas an intensification of the hydrological cycle is predicted in summer and autumn. Temperature is predominantly affected over the Iberian Peninsula and the eastern part of the MTR. In many grid boxes, the signals are blurred out due to the large amount of uncertainty in the EV estimate. Thus, a careful analysis is required when making inferences about the future behaviour of climate extremes.

A Bayesian decision method for climate change signal analysis

In this paper we would like to put forward another view of climate change signal analysis, namely a data based decision or selection process of specific scenarios (or hypotheses in statistical language). Given at least two scenarios (1) a control one (CTL) from unforced climate model simulations and (2) a set of climate model realizations under an identical external climate forcing, a climate change signal analysis can also be viewed as a decision or selection process of the scenario which is most probable in view of the observations. It is shown that our approach includes the classical fingerprint method. The approach is based on Bayesian decision theory. The selection of hypothesis/scenario uses the Bayes factors. It is exemplified with NCEP/NCAR reanalysis data from 1979 to 1999 as observation, a greenhouse-gas forced scenario (G) with four realizations, and a greenhouse-gas plus sulfate aerosol forced scenario (GS) with two realizations generated by the ECHAM3/LSG from 1880 to 2049. The CTL scenario is obtained from an unforced control run of ECHAM3/LSG of 1400 years length. To provide a vivid example, Northern Hemisphere extratropics area-averaged and 13-month running mean 2 m and 70 hPa temperature anomalies are selected as analysis variables. The Northern Hemisphere extratropics from 1979-1999 are considered as the most reliable area of NCEP/NCAR reanalysis data and the two dimensional setting provides valuable insight into the results. The decision test between the CTL and G scenarios shows that observations of near surface and lower stratosphere temperatures provide evidence against the CTL scenario since the late 1990s even if the prior probability for the G scenario is a third of that of the CTL scenario. The second decision experiment with a three-scenario case shows that both climate change scenarios (G and GS) have higher evidence in view of the observations of the late 1990s than the CTL scenario. A sensitivity analysis with respect to the strength of the natural variance in observations indicates that the specification of the level of the observed natural variability is a crucial factor in the Bayesian decision. In both decision experiments some of the decisions are based upon observations which have very small likelihood value given any of the scenarios, which might be due to the omission of stratospheric ozone forcing in climate change simulations or volcanic/solar forcing in the no change scenario simulation.